Parallel Processing of Image Segmentation Data Using Hadoop

The use of sequential programming is slowly getting replaced by distributed and parallel computing which is widely being used in computing industries to handle tasks with big data and various high-end computing applications comprising of huge image and video data banks. Moreover, image processing using parallel computation is also gaining momentum in today's technological era. Nowadays researchers are coming up with various methodologies to tackle high scale image processing applications by implementing parallel computing methodologies to carry out the specified image processing application task and simultaneously checking its performance against sequential programming. At the same time there are constraints on what can be done to maximize the task performance using high end multi-core CPU's with advanced buses and interconnects that offer high bandwidth with low system latency. It is to be noted that there is no availability of standardized image processing task which can be used to evaluate a single node system. In this paper, we propose an efficient parallel processing algorithm to perform the task of image segmentation with the foremost aim to analyze the threshold of data size at which the proposed method outperforms sequential programming method in terms of task execution time by analyzing the distribution of average CPU cores usage and its threads over the execution time. The proposed methodology could be useful for researchers, as it can perform multiple image segmentation in parallel, which can save a lot of time of the user. For the purpose of comparison, we also implemented the same image segmentation task using sequential method of programming in an integrated development environment platform

Automated Odour Measurement In Electronic Nose System Using Microcontroller.

This paper presents an automated odour measurement process using AT89C55WD Microcontroller for the electronic nose (e-nose) system

New Enhanced Artificial Bee Colony (JA-ABC5) Algorithm with Application for Reactive Power Optimization

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement

Dorsal hand vein image enhancement using fusion of clahe and fuzzy adaptive gamma

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins

Evaluating the Surface Free Energy and Moisture Sensitivity of Warm Mix Asphalt Binders Using Dynamic Contact Angle

From the environmental conservation perspective, warm mix asphalt is more preferable compared to hot mix asphalt. This is because warm mix asphalt can be produced and paved in the temperature range 20–40°C lower than its equivalent hot mix asphalt. In terms of cost-effectiveness, warm mix asphalt can significantly improve the mixture workability at a lower temperature and thus reduce greenhouse gas emissions, to be environment friendly. However, the concern, which is challenging to warm mix asphalt, is its susceptibility to moisture damage due to its reduced production temperature. This may cause adhesive failure, which could eventually result in stripping of the asphalt binder from the aggregates. This research highlights the significance of Cecabase warm mix additive to lower the production temperature of warm mix asphalt and improvise the asphalt binder adhesion properties with aggregate. The binders used in the preparation of the test specimen were PG-64 and PG-76. The contact angle values were measured by using the dynamic Wilhelmy plate device. The surface free energy of Cecabase-modified binders was then computed by developing a dedicated algorithm using the C++ program. The analytical measurements such as the spreadability coefficient, work of adhesion, and compatibility ratio were used to analyze the results. The results inferred that the Cecabase improved the spreadability of the asphalt binder over limestone compared to the granite aggregate substrate. Nevertheless, the Cecabase-modified binders improved the work of adhesion. In terms of moisture sensitivity, it is also evident from the compatibility ratio indicator that, unlike granite aggregates, the limestone aggregates were less susceptible to moisture damage

Cycle time minimization in production line using robust hybrid optimization algorithm

Bio-inspired algorithms that have been introduced by mimicking the biological phenomenon of nature have widely implemented to cater various real-world problems. As example, memetic algorithm, EGSJAABC3 is applied for economic environmental dispatch (EED) optimization, Hybrid Pareto Grey Wolf Optimization to minimize emission of noise and carbon in U-shaped robotic assembly line and Polar Bear Optimization to optimize heat production. The results obtained from their research have clearly portrayed the robustness of bio-inspired algorithms to cater complex problems. Assembly line, which is normally the last step of production that involves final assembly of the products. An assembly line generally consists of several workstations placed in sequential order. Each of the workstation is in charge to complete certain specific jobs. Hence, it is a must to make the best use of the efficiency of the assembly line. Cycle time minimization is part of the assembly line balancing problem due to its uncertainty that dependent on the number of manpower, material preparation and machine capacity. Cycle time basically means time needed to process a product using a specific task in a production line. This project proposes the application of new hybrid optimization algorithm named JAABC5-RRO to minimize cycle time to produce a new audio product on a production line in a production company

A realization of classification success in multi sensor data fusion

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991)

Principal Component Analysis – A Realization of Classification Success in Multi Sensor Data Fusion

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991)

Parallel Processing of Image Segmentation Data Using Hadoop

The use of sequential programming is slowly getting replaced by distributed and parallel computing which is widely being used in computing industries to handle tasks with big data and various high-end computing applications comprising of huge image and video data banks. Moreover, image processing using parallel computation is also gaining momentum in today's technological era. Nowadays researchers are coming up with various methodologies to tackle high scale image processing applications by implementing parallel computing methodologies to carry out the specified image processing application task and simultaneously checking its performance against sequential programming. At the same time there are constraints on what can be done to maximize the task performance using high end multi-core CPU's with advanced buses and interconnects that offer high bandwidth with low system latency. It is to be noted that there is no availability of standardized image processing task which can be used to evaluate a single node system. In this paper, we propose an efficient parallel processing algorithm to perform the task of image segmentation with the foremost aim to analyze the threshold of data size at which the proposed method outperforms sequential programming method in terms of task execution time by analyzing the distribution of average CPU cores usage and its threads over the execution time. The proposed methodology could be useful for researchers, as it can perform multiple image segmentation in parallel, which can save a lot of time of the user. For the purpose of comparison, we also implemented the same image segmentation task using sequential method of programming in an integrated development environment platform