Macman database

The Macintosh revolutionary interface sets it apart from all other personal computers. The Macintosh designers made the Macintosh easy to learn, understand, and use. To ensure correct implementation of their User interface, Apple provided Macintosh programmers with "Inside Macintosh". Inside Macintosh is a complete detailed manual which contains recommendations and outlines the operation of menus, windows, resources, etc. as well as some hints as to the appearance and flow of an application

A new strategy for processors allocation in an n-cube multiprocessor

In this paper we will describe two known strategies for static processors allocation in an n-cube multiprocessor, namely the buddy system strategy and the gray code strategy and then propose a new strategy that outperforms the first by (n-k+1) and the second by (n-k+1)/2 in cube recognition. Furthermore, our strategy is suitable for static as well as dynamic processors allocation and it results in a less system fragmentation, more subcubes recognition, and higher fault tolerance. We also introduce an extension to our strategy that will enhance the performance drastically so that our algorithm together with the extension will outperform the buddy system by a factor of [k(n-k)+1] and the gray strategy by [k(n-k)+1]/2 in cube recognition. The implementation details of these algorithms are also described

Efficient fault tolerant broadcasting algorithm for the hypercube

One of the most popular topologies is the hypercube, that has n = 2ᵏ processors, numbered 0 to 2ᵏ -1 and connected in such a way that there is a link between any two if and only if they differ in one bit. Its popularity is due to the fact that a large number of processors can be interconnected using a small number of communication links while at the same time keeping the communication delay between processors at minimum. Broadcasting is a procedure by which a processor can pass a message to all other processors in the network non redundantly; it is extremely important for diagnosis of the network, distribution agreement or clock synchronization. In this paper we present some interesting features and properties of the hypercube and then we describe the algorithm for broadcasting in the hypercube, by Sullivan and Bashkow. Finally, we develop a simple yet efficient algorithm for broadcasting m the hypercube in the presence of some faulty processors

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques

Mixed-Flow Load-Balanced Scheduling for Software-Defined Networks in Intelligent Video Surveillance Cloud Data Center

As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput

Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning

Sign prediction problem aims to predict the signs of links for signed networks. Currently it has been widely used in a variety of applications. Due to the insufficiency of labeled data, transfer learning has been adopted to leverage the auxiliary data to improve the prediction of signs in target domain. Existing works suffer from two limitations. First, they cannot work if there is no target label available. Second, their generalization performance is not guaranteed due to that fact that the solution of their objective functions is not global optimal solution. To solve these problems, we propose a novel sign prediction on unlabeled social networks using branch and bound optimized transfer learning (SP_BBTL) sign prediction model. The main idea of SP_BBTL is to use target feature vectors to reconstruct source domain feature vectors based on relationship projection, which is a complicated optimal problem and is solved by proposed optimization based on branch and bound that can obtain global optimal solution. With this design, the target domain label information is not required for classifier. Finally, the experimental results on the large scale social signed networks validate the superiority of the proposed model

Distributed Energy-Efficient Approaches for Connected Dominating Set Construction in Wireless Sensor Networks

Energy efficiency is one of the major issues in wireless sensor networks (WSNs) and their applications. Distributed techniques with low message and time complexities are expected in WSNs. Connected dominating sets (CDSs) have been widely used for virtual backbone construction in WSNs to control topology, facilitate routing, and extend network lifetime. Most of the existing CDS approaches suffer from a very poor approximation ratio, high time, and message complexities. This paper proposes two novel approaches for CDS distributed construction in WSNs. The proposed approaches are intended to construct a small CDS as well as allowing energy-efficient CDS construction and maintenance in WSNs. Simulation shows that our distributed approaches have an approximation factor of 7.5 to the optimal CDS. This approximation outperforms the existing distributed CDS construction algorithms