Mobility management architectures based on joint mobile IP and SIP protocols

In the all-IP wireless networks beyond the third generation, mobility management can be effectively achieved by applying mobile IP (MIP) and the session initiation protocol (SIP) jointly. Nevertheless, an efficient combination of both protocols remains an open research issue. Conventional hybrid MIP-SIP mobility architectures operate MIP and SIP almost independently, resulting in significant redundant costs. This article investigates the representative hybrid MIP-SIP architectures and explores the joint optimizations between MIP and SIP for a more cost-efficient mobility support whilst utilizing their complementary power. Two novel design approaches are presented. The first approach culminates in a tightly integrated architecture, which merges the redundant mobility entities in MIP and SIP to yield maximum system efficiency. The other approach leads to a loosely integrated architecture, where necessary interactions are introduced between MIP and SIP mobility servers while their physical entities are kept intact. Major mobility procedures, including location update, session setup and handoff, are discussed in these architectures. The analytical results demonstrate that both proposed architectures outperform typical hybrid MIP-SIP architectures in terms of clear-cut reduced signaling costs     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Polarized light reflectometer

The modulated reflected and nonreflected light fluxes, measured as the azimuth of incident linearly polarized light is varied, yield the absolute reflectances R(p), and R(s), of a dielectric or semiconductor surface. Application to a reflective Si detector determines the refractive index and thickness of a SiO(2) film on the detector surface.     

Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Neural Computing and Applications - Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model...     

New aspect-oriented constructs for security hardening concerns

In this paper, we present new pointcuts and primitives to Aspect-Oriented Programming (AOP) languages that are needed for systematic hardening of security concerns. The two proposed pointcuts allow to identify particular join points in a program's control-flow graph (CFG). The first one is the GAFlow, Closest Guaranteed Ancestor, which returns the closest ancestor join point to the pointcuts of interest that is on all their runtime paths. The second one is the GDFlow, Closest Guaranteed Descendant, which returns the closest child join point that can be reached by all paths starting from the pointcut of interest. The two proposed primitives are called ExportParameter and ImportParameter and are used to pass parameters between two pointcuts. They allow to analyze a program's call graph in order to determine how to change function signatures for passing the parameters associated with a given security hardening. We find these pointcuts and primitives to be necessary because they are needed to perform many security hardening practices and, to the best of our knowledge, none of the existing ones can provide their functionalities. Moreover, we show the viability and correctness of the proposed pointcuts and primitives by elaborating and implementing their algorithms and presenting the result of explanatory case studies.