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%.

     

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...     

Chaotic encryption of speech signals

This paper introduces a speech encryption approach, which is based on permutation of speech segments using chaotic Baker map and substitution using masks in both time and transform domains. Two parameters are extracted from the main key used in the generation of mask. Either the Discrete Cosine Transform (DCT) or the Discrete Sine Transform (DST) can be used in the proposed cryptosystem to remove the residual intelligibility resulting from permutation and masking in time domain. Substitution with Masks is used in this cryptosystem to fill the silent periods within speech conversation and destroy format and pitch information. Permutation with chaotic Baker map is used in to maximize the benefits of the permutation process in encryption by using large-size blocks to allow more audio segments to be permutated. The proposed cryptosystem has a low complexity, small delay, and high degree of security. Simulation results prove that the proposed cryptosystem is robust to the presence of noise.     

Distribution and photodynamic effect of zinc phthalocyanine disulfonate in nude mice bearing mammary carcinoma

The distribution study of zinc phthalocyanine disulfonate (ZnPcS2) in nude mice bearing mammary carcinoma (T50/80) revealed a rapid uptake of the dye by tumor. In experimental photodynamic therapy (PDT), the tumors were exposed to laser radiation (670 nm, 100 mW/cm2, 150 J/ /cm2) after intravenous administration of ZnPcS2 in saline. The results showed the maximum tumor destruction to be achieved for the time interval between injection of the drug (2 mg/kg) and exposure to laser light of 5 min, while a significantly less damage was observed when the time interval was 24 h (p < 0.0001). The degree of damage produced by the treatment was monitored in vivo by means of noninvasive NMR-imaging and subsequently confirmed histologically.     

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.     

Performance evaluation and implementation of convolution coded OFDM modem in wireless underwater acoustic communication

High‐data transmission rate and reliable communication in underwater acoustic channel is challenging because of limited bandwidth, multipath propagation, and Doppler shift, which results in poor bit error performance. Under this constraint, this paper explains the simulation results of underwater wireless acoustic data transmission system by using quadrature phase shift keying modulation with convolution encoder at the transmitter and proposed Viterbi decoder at the receiver. The decoder algorithm in comparison with MATLAB inbuilt function shows asserting improved results. This paper evaluates the performance of convolution coded orthogonal frequency division multiplexing modem, which is studied over typical underwater channel through an extensive computer simulation and a semirealistic experimentation. The performance of convolution coded orthogonal frequency division multiplexing system is measured in time domain plots, bit error rate curves, and number of bit errors per frame over additive white Gaussian noise and Rayleigh channel conditions.     

A New Method Modifying Single Miller Feedforward Frequency Compensation to Drive Large Capacitive Loads: Putting an Attenuator in the Path

A new method to compensate three-stage amplifier to drive large capacitive loads is proposed in this paper. Gain Bandwidth Product is increased due to use an attenuator in the path of miller compensation capacitor. Analysis demonstrates that the gain bandwidth product will be improved significantly without using large compensation capacitor. Using a feedforward path is deployed to control a left half plane zero which is able to cancel out first non-dominant pole. A three stage amplifier is simulated in a 0.18 μm CMOS technology. The purpose of the design is to compensate three-stage amplifier loading 1000 pF capacitive load. The simulated amplifier with a 1000 pF capacitive load is performed in 3.3 MHz gain bandwidth product, and phase margin of 50. The compensation capacitor is reduced extremely compared to conventional nested miller compensation methods. Since transconductance of each stage is not distinct, and it is close to one another; as a result, this method is suitable low power design methodology.     

Metabolomics-assisted biotechnological interventions for developing plant-based functional foods and nutraceuticals

Today, the dramatic changes in types of food consumed have led to an increased burden of chronic diseases. Therefore, the emphasis of food research is not only to ensure quality food that can supply adequate nutrients to prevent nutrition related diseases, but also to ensure overall physical and mental-health. This has led to the concept of functional foods and nutraceuticals (FFNs), which can be ideally produced and delivered through plants. Metabolomics can help in getting the most relevant functional information, and thus has been considered the greatest –OMICS technology to date. However, metabolomics has not been exploited to the best potential in plant sciences. The technology can be leveraged to identify the health promoting compounds and metabolites that can be used for the development of FFNs. This article reviews (i) plant-based FFNs-related metabolites and their health benefits; (ii) use of different analytic platforms for targeted and non-targeted metabolite profiling along with experimental considerations; (iii) exploitation of metabolomics to develop FFNs in plants using various biotechnological tools; and (iv) potential use of metabolomics in plant breeding. We have also provided some insights into integration of metabolomics with latest genome editing tools for metabolic pathway regulation in plants.     

Secure image transmission through crypto‐OFDM system using Rubik's cube algorithm over an AWGN channel

Confidentiality of information must be maintained when it is required to be transmitted over a communication channel or when it is stored in a computer for further information access. Establishing a highly reliable and secure means of wireless communication for the transfer of digital data (text, images, audio, and video) from source to destination is becoming a prime requirement in present‐day wireless communications. Security can be achieved at the network level as well as at the data level. Data‐level security is made through cryptographic techniques. This paper emphasizes data‐level security aspects in a wireless communication system for the secure transmission of images over an AWGN channel condition. Here, a crypto orthogonal frequency division multiplexing system (crypto‐OFDM system) is designed using Rubik's cube encryption algorithm scheme for secured transmission of images under the MATLAB environment. The quality of the image transmission is analyzed using peak signal to noise ratio (PSNR) and BER at different SNR conditions. The quality of the encryption algorithm was also tested with statistical metrics, which are the histogram, NPCR, UACI, entropy, correlation coefficient analysis, etc. The numerical results reveal that a DCT‐based crypto‐OFDM system with Rubik's cube algorithm show a better performance over earlier cryptosystems and also perform superior to the original/basic OFDM system. The statistical analysis tests prove that Rubik's cube algorithm is one of the most robust algorithms for security point of view and is easy to implement, as Rubik's cube algorithm makes use of natural characteristics of pixel values, which form the basis for designing the secret key .