Asynchronous operations in distributed concurrency control

Concurrency control is an integral component in achieving high performance in many-core databases. Implementing serializable transaction processing efficiently is challenging. One approach, serialization graph testing (SGT) faithfully implements the conflict graph theorem by aborting only those transactions that would actually violate serializability (introduce a cycle), thus maintaining the required acyclic invariant. Alternative approaches, such as two-phase locking, disallow certain valid schedules to increase throughput, whereas SGT has the theoretically optimal property of accepting all and only conflict serializable schedules. Historically, SGT was deemed unviable in practice due to the high computational costs of maintaining an acyclic graph. Research has however overturned this historical view by utilising the increased computational power available due to modern hardware. Furthermore, a survey of 24 databases suggests that not all transactions demand conflict serializability but different transactions can perfectly settle for different, weaker isolation levels which typically require relatively lower overheads. Thus, in such a mixed environment, providing only the isolation level required of each transaction should, in theory, increase throughput and reduce aborts. The aim of this paper is to extend SGT for mixed environments subject to Adya’s mixing-correct theorem and demonstrate the resulting performance improvement. We augment the YCSB benchmark to generate transactions with different isolation requirements. For certain workloads, mixed serialization graph testing can achieve up to a 28% increase in throughput and a 19% decrease in aborts over SGT.

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

Edge detection is a fundamental image processing technique used to spot sudden shifts in color or intensity in image. It is utilized to detect and highlight boundaries between various items or regions in image, as well as to detect features such as corners, circles and lines. Edge detection approaches typically work by applying a filter to an image to detect areas where the image undergoes an immediate shift in magnitude. Applications for edge detection techniques include recognizing objects, healthcare images, and background segmentation. Many techniques have been presented based on the classical approaches (such as Sobel, Prewitt, and Roberts, Canny, Laplacian of Gaussian (LOG), etc.) and soft computing approaches (SCA), which are the two main approaches for detection of edge. This paper provides an overview of studies carried out on edge detection using various approaches. That will assist brand-new researchers in learning about these techniques and selecting one from among them to evolve or improve according to their field of study.

In this paper, a fast multi-scale edge detection approach is proposed based on the theories of image diffusion and curve evolution. In comparison with the previous edge detection approaches, edge detection is performed in two steps: beginning by detecting the initial contours at a courser scale that is performed by using a linear diffusion coupling with the denoising effect and gray transformation, then the obtained contour curves are mapped and further refined up to finer scales using the fast Hermes algorithm. By this way, the experimental results show that the proposed edge detection approach are more promising than the existing methods for object detection on general images, especially on medical images.

Localization of edge points in images is one of the most important starting steps in image processing. Many varied edge detection techniques have been proposed. Different edge detectors present distinct and different responses to the same image, showing different details. This work presents a new approach for edge detection. The actual gray level image is locally thresholded using the local mean value to make a binary image. The binary image is checked for edges by comparing with the known edge like patterns, utilizing Boolean algebra. This approach recognizes nearly all, real edges and edges due to noise. For removing edges due to noise, we adopt another approach. This time the actual image is globally thresholded by the variance value of the image. The two resulting images are logically ANDed to get the final edge map.

Purpose – The purpose of this paper is to present a new approach to edge detection using semiconductor flash memory networks having scalable and parallel hardware architecture. Design/methodology/approach – A flash cell can store multiple states by controlling its voltage threshold. The equivalent resistance of the operation states controlled by threshold voltage of flash cell gives out different combinations of logic 0 and 1 states. The paper explores this basic feature of flash memory in designing a resistance change memory network for implementing novel edge detector hardware. This approach of detecting the edges is inspired from the spatial change detection ability of the human visual system. Findings – The proposed approach consumes less number of electronic components for its implementation, and outperforms the conventional approaches of edge detection with respect to the processing speed, scalability and ease of design. It is also demonstrated to provide edges invariant to changes in the direction of the spatial change in the images. Research limitations/implications – This research brings about a new direction in the development of edge detection, in terms of developing high-speed parallel processing edge detection and imaging circuits. Practical implications – The proposed approach reduces the implementation complexity by removing the need to have convolution operations for spatial edge filtering. Originality/value – This paper presents one of the first edge detection approaches that is purely a hardware oriented design, uses resistance of flash memory to form edge detector cells, and one that does not use computational operations such as additions or multiplications for its implementation.

This paper proposes a new approach for edge detection using a combination of artificial bee colony (ABC) algorithm and an improved derivative technique. ABC algorithm simulates the foraging behavior of a honey bee swarm. The proposed approach find the edge pixels by an improved derivative technique to compute fitness function, and then applying ABC algorithm determine the most fit pixels to be considered as the edge pixels. Qualitative and quantitative analysis of the proposed approach and its comparison with other standard edge detection methods are presented. Shannon’s entropy function and Pratt’s figure of merit are used for quantitative analysis. The effect of variation of parameters on performance of the proposed approach is discussed. Experimental results show that proposed method outperformed most of existing techniques.

This paper present a new approach for edge detection using gravitational search algorithm (GSA) and universal law of gravity. The direct application of any edge detection algorithm on an entire image requires a huge memory which leads to optimization problems. Gravitational search algorithm is an optimization algorithm inspired by Newtonian gravity. Objects in space attract each other by way of gravity force, and this force causes a global movement of all objects towards the objects with heavier masses. Hence, masses cooperate using a direct form of communication, through gravitational force. In the proposed approach the edges are detected using the law of universal gravity and movement of agents are computed using gravitational search algorithm. The proposed approach is able to detect the edge pixel in an image up to a certain extent. The technique can be further extended for finding more edge pixels by modifying the gravitational search algorithm.

High speed image processing is becoming increasingly important in medical imaging. Using the state-of-the-art ZYNQ-7000 system on chip (SoC) has made it possible to design powerful vision systems running software on an ARM processor and accelerating it from hardware resources on a single chip. In this paper, we take the advantage of accelerating an embedded system design on a single SoC, which offers the required features for real-time processing of skin cancer images. Different edge detection approaches such as Sobel, Kirsch, Canny and LoG have been implemented on ZYNQ-7000 for border detection of skin lesions, which can be used in early diagnosis of melanoma. The results show that the extended 5 × 5 canny edge detection implemented on the proposed embedded platform has better performance in compare with other reported methods. The performance evaluation of this approach has shown good processing time of 60 fps for real time applications.

Descriptions of the structure of a picture or scene generally refer to objects or regions in the scene. Thus, to construct a description, it is generally necessary to segment the scene into meaningful parts. When the parts have gray levels (or colors) that lie in different ranges, this is easy to do by simple thresholding, or by edge detection using simple differencing operations. When the parts differ in texture, rather than in gray level, the thresholding and edge detection approaches can be generalized to work in a variety of cases; the basic approach involves preprocessing and averaging prior to the thresholding or differencing step. This approach is based on the assumption that textural differences between regions are generally associated with differences in the average values of some simple local property. Examples of segmenta-tions obtained by these methods will be given.© (1976) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this fast-paced world, it is very challenging for the elderly and disabled population to move independently to their desire places at any convenient time. Fortunately, some of the people have good eyesight and physically strong to take care of their survival. Nevertheless, Electric wheelchair (EWC) can provide them a better lifestyle with commendable confidence. At the same time, the hand, head and voice recognition-based EWC meet many limitations. Despite, the eye-tracking-based EWC provides a better smartness in their lifestyle. This research article discusses better accuracy achievement and minimizes the delay response time in the proposed system. The proposed eye-tracking EWC is differed from another existing system with good validation parameters of the controller and it introduces edge detection to identify the eye pupil position in the face. The proposed method includes a PID controller to control the DC motor, which in turn controls the rotation of wheel in EWC. This research article is mainly focused on the cost-effectiveness and improvement in the system performance. The display system is mounted in front of the sitting position of EWC users. The camera captures eye pupil position and it determines the direction of the EWC movement by controlling DC motor with the help of a PID controller. When derivative (D) control is used in the proposed system, the system response is quite faster and it reduces the delay time between the user and system reaction. This pupil of eye position is determined by a canny edge detector, which provides good results when compared with other edge detection approaches. Object detection in front of the EWC is an added advantage of the proposed system. The proposed article integrates all the activities and measures the system performance. The proposed model achieves an accuracy of about 90% and response time is least compared with the existing methods.

Cumulonimbus clouds is one of the early formations that leads to a small scale tornado known locally in Indonesia as waterspout. The clouds cumulonimbus shows some irregular and undefined phenomena called chaos. However, in order to make sure that a chaos phenomenon will lead to the occurance of tornadoes, some criteria and characteristics should be fulfilled. One of such criteria is an extreme heat that is caused by the formation of clouds cumulonimbus. On the gray color edge of the cumulonimbus clouds there are some conditions that characterize the image such as wavelength, frequency and the intensity of the colors in clouds cumulonimbus images, as well as the different levels of the brightness and darkness. In addition, color of the image could be used also as a basis to define the parameters related to the start condition of the tornado. In this research, edge detection algorithm is used to get the gray edge boundary, with mean to extract the intensification of irregular patterns on cumulonimbus clouds in order to automatically predict the occurrence of tornadoes. The results of this research show that the edge detection approach is a promising technique for prediction of tornado occurrence.

Advances in medical imaging modalities have made it possible to acquire volume images. One of the key steps used to split the raw volume image into meaningful sub-volumes is 3D edge detection. This paper describes a new approach for 3D edge detection. It is based on the 2D hybrid edge detector, which consists of a combination of the first and second order differential edge detectors. It was shown in the 2D case that the combination of the two differential edge detectors gave an accurate edge localisation whilst maintaining immunity to the noise in the image. Results using the 3D hybrid edge detector based on synthetic and real images are presented. They are also compared to the results using the 3D gradient of the Gaussian detector and the 3D Laplacian of the Gaussian detector.

Based on mathematical morphology of omni directional multi-scale element, an algorithm of image edge- detection was proposed. Mathematical morphology of omni directional multi-scale element is defined in order to suppress noise and adapt to different edge in the image. An approach of image edge detection based on morphology of omni directional multi-scale element is constructed by power adding combination of morphological operation. The results of simulation in medical image demonstrate that the method performs better not only in edge detection but also in noise-suppression than classical edge detection operator.

A novel approach of combined edge detection and segmentation for tool wear measurement in machining