Mueen Uddin

Medical Image Segmentation Methods, Algorithms, and Applications

ABSTRACT Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.

Improved quadtree image segmentation approach to region information

Abstract Images are full of information and most often, little information is desired for subsequent processing. Hence, region of interest has key importance in image processing. Quadtree image segmentation has been widely used in many image processing applications to locate the region of interest for further processing. There are also variable block-size image coding techniques to effectively reduce the number of transmitted parts. This paper presents quadtree partition technique as a pre-processing step in image processing to determine what part should be more heterogeneous than the others. It also introduces an idea to solve the problem of squared images. Finally, proposed approach is implemented and analysed. The simulation of the Matlab code of the quadtree is represented by all algorithms and the figures. Thus, achieved results are promising in the state of the art.

Annotated comparisons of proposed preprocessing techniques for script recognition

Offline cursive script recognition and their associated issues are still fresh despite of last few decades’ research. This paper presents an annotated comparison of proposed and recently published preprocessing techniques with reported work in the offline cursive script recognition. Normally, in the offline script analysis, the input is a paper image or a word or a digit and the desired output is ASCII text. This task involves several preprocessing steps, and some of them are quite hard such as line removal from text, skew removal, reference line detection (lower/upper baselines), slant removal, scaling, noise elimination, contour smoothing and skeleton. Moreover, subsequent stage of segmentation (if any) and recognition is also highly dependent on these preprocessing techniques. This paper presents an analysis and annotated comparison of latest preprocessing techniques proposed by authors with those reported in the literature on IAM/CEDAR benchmark databases. Finally, future work and persist problems are highlighted.

Dynamic packet beaconing for GPSR mobile ad hoc position-based routing protocol using fuzzy logic

Greedy perimeter stateless routing (GPSR) is a well-known position-based routing protocol in mobile ad hoc network (MANET). In GPSR, nodes use periodic beaconing (PB) strategy in broadcasting beacon packets to maintain up-to-date information on the geographical position of their neighbor nodes within their transmission boundary range. The nodes that receive beacon packets save all known neighbor nodes, along with their geographical position information, in their neighbor list to make effective routing decisions. Most recent studies on position-based routing protocols assume that the position information in the neighbor list of a node is accurate, although only a rough estimate of such position information is actually available to the node. Node mobility causes frequent network topology changes in MANETs. Thus, neighbor-to-neighbor relationships change frequently. Using the PB strategy leads to inaccurate node position information in the neighbor list of a node. In addition, it may cause the routing protocol to make suboptimal decisions and not route packets through the best-located neighbor within the transmission range of the node. This study presents an analysis of the influence of position information inaccuracy caused by network parameters such as beacon packet interval time (BPIT) and node moving speed (NMS) on the performance of GPSR position-based routing protocols. To overcome the effect of position information inaccuracy in the neighbor list of a node, we proposed a fuzzy logic dynamic beaconing (FLDB) strategy to improve the reliability of the neighbor list of a node by optimizing time between transmissions of beacon packets in position-based routing protocols. Optimization is based on the correlation between NMS, number of neighboring nodes (NoNNs) and BPIT using fuzzy logic control (FLC) mechanism. The simulation experiment shows the effectiveness of the FLC mechanism in improving overall performance of GPSR position-based routing protocol in terms of beacon packet control overhead, end-to-end delay, non-optimal hop, and false node position.

Cloud Computing Adoption Model for Universities to Increase ICT Proficiency

Universities around the world especially those in developing countries are faced with the problem of delivering the level of information and communications technology (ICT) needed to facilitate teaching, learning, research, and development activities ideal in a typical university, which is needed to meet educational needs in-line with advancement in technology and the growing dependence on IT. This is mainly due to the high cost involved in providing and maintaining the needed hardware and software. A technology such as cloud computing that delivers on demand provisioning of IT resources on a pay per use basis can be used to address this problem. Cloud computing promises better delivery of IT services as well as availability whenever and wherever needed at reduced costs with users paying only as much as they consume through the services of cloud service providers. The cloud technology reduces complexity while increasing speed and quality of IT services provided; however, despite these benefits the challenges that come with its adoption have left many sectors especially the higher education skeptical in committing to this technology. This article identifies the reasons for the slow rate of adoption of cloud computing at university level, discusses the challenges faced and proposes a cloud computing adoption model that contains strategic guidelines to overcome the major challenges identified and a roadmap for the successful adoption of cloud computing by universities. The model was tested in one of the universities and found to be both useful and appropriate for adopting cloud computing at university level.

Techniques to implement in green data centres to achieve energy efficiency and reduce global warming effects

Energy demand in data centre industry is growing rapidly as computing technology changes and Information Technology (IT) professionals seek to maximise performance of data centres. A multitude of methods have been used to estimate and quantify energy intensity. Rising energy costs, continuing concerns about global economic downturn and global warming effects has leaded ways for energy efficient data centres. The goal of computer system design has been shifted to power and energy efficiency. This paper highlights strategies and techniques that provide energy savings in data centre like reducing frequent hardware purchases, power/cooling cost reductions, green metrics, shrinking data storage, workload consolidation and reducing physical servers.

Carbon sustainability framework to reduce CO 2 emissions in data centres

Reducing the impact of greenhouse gases (CO2) on climate change in an attempt to tackle global warming issues is a challenge many companies and businesses are trying to undertake. Businesses across the globe need to take a leadership role in reducing their carbon footprint. This paper proposes a new IT-based carbon sustainability framework that provides a comprehensive strategy to reduce the emission of green house gases (CO2) from data centres in order to reduce the effects of global warming. The proposed IT sustainability framework reduces the overall cost of ownership in data centres by employing new technologies to increase the use of already installed servers and proposes metrics to measure the efficiency of data centres from time to time in terms of energy consumptions and CO2 emissions. The proposed framework also increases the awareness of implementing metrics and highlights the development of a sustainability strategy for data centre managers to implement energy saving and CO2 emission techniques to enable them to increase productivity and help businesses gain a competitive edge.

Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc Network Using the Fitness Function

Mobile ad hoc network (MANET) is a collection of wireless mobile nodes that dynamically form a temporary network without the reliance of any infrastructure or central administration. Energy consumption is considered as one of the major limitations in MANET, as the mobile nodes do not possess permanent power supply and have to rely on batteries, thus reducing network lifetime as batteries get exhausted very quickly as nodes move and change their positions rapidly across MANET. This paper highlights the energy consumption in MANET by applying the fitness function technique to optimize the energy consumption in ad hoc on demand multipath distance vector (AOMDV) routing protocol. The proposed protocol is called AOMDV with the fitness function (FF-AOMDV). The fitness function is used to find the optimal path from source node to destination node to reduce the energy consumption in multipath routing. The performance of the proposed FF-AOMDV protocol has been evaluated by using network simulator version 2, where the performance was compared with AOMDV and ad hoc on demand multipath routing with life maximization (AOMR-LM) protocols, the two most popular protocols proposed in this area. The comparison was evaluated based on energy consumption, throughput, packet delivery ratio, end-to-end delay, network lifetime and routing overhead ratio performance metrics, varying the node speed, packet size, and simulation time. The results clearly demonstrate that the proposed FF-AOMDV outperformed AOMDV and AOMR-LM under majority of the network performance metrics and parameters.

Effects of Stocking Density on Production and Economics of All-Male Tilapia (Oreochromis niloticus) Culture in a Rain Fed Rice-Fish Ecosystem

Abstract Effects of stocking density on the growth, production, and economics of all-male tilapia (Oreochromis niloticus) were investigated in a rain-fed rice-fish ecosystem for a period of 120 days. Fish were stocked at the rate of 4000, 5000, and 6000 ha−1 in treatments T1, T2, and T3, respectively. Water quality was suitable for fish culture. Significantly higher growth was observed in T1 as compared to other treatments. SGR ranged from 1.26 to 1.51. Survival varied between 79% and 88% with treatment T1 producing the highest survival. The highest benefit:cost ratio was obtained in T2 followed by T1 and T3. The best fish production (457.21 kg ha−1) and economic return for the culture period was obtained at 5000 fish ha−1.

Criteria to select energy efficiency metrics to measure performance of data centre

The progress and expansion of data centre industry is driven by ever growing business domains, end user applications from consumers and performance improvement limitations. Due to continuous increase in the size and magnitude of these tier level data centres there is a continuous demand for energy consumption and emission of green house gases that has started to limit further performance progression due to overwhelming electricity bills and global warming effects hazardous for global health and environmental sustainability. The research highlighted in this paper discusses about instigates and problems of high power/energy consumption, and explores the reasons why energy savings in data centre is an important issue. It then spotlights the importance of implementing green metrics for measuring efficiency of data centre in terms of energy consumption and CO2 emissions. Metrics are instruments used to measure the performance of data centre. We proposed a methodology to choose appropriate metrics suitable for measuring data centre efficiency and performance in terms of energy efficiency, cost savings, green initiatives and CO2 emissions. The proposed methodology helps data centre managers to select appropriate metrics and then set benchmarks.