Analysis of two loops for carrier recovery in CPM with index 1/2

An examination is made of the performance of a squaring loop and a fourth-power loop used for tracking the received carrier phase of binary continuous-phase modulated (CPM) signals with index 1/2. Noise-free analysis is performed first and a simple relationship between the phase error and the control voltage is obtained for each case, with the premodulation filter response as a parameter. Linearized analysis of the phase error variance is then conducted in the presence of additive white Gaussian noise. It is shown that the phase variance in both cases contains a self-noise component that sets a lower bound on the synchronizer's performance at very high signal-to-noise ratios. Numerical results are presented for a number of popular CPM signals. It is found that the squaring loop performs best with MSK (minimum shift keying), while the fourth-power loop performs best with duobinary MSK     

Isolation and characterization of a lectin with antifungal activity from Egyptian Pisum sativum seeds

A plant lectin isolated in its pure state from the Egyptian seeds of Pisum sativum (PSL) produced two bands in SDS–PAGE (5.53 and 19.3 kDa; i.e. α and β chain) but one peak by gel filtration chromatography on Sephadex G-100, corresponding to 50 kDa, i.e., a dimeric structure of two monomers, each consisting of one α and one β subunit. PSL is a glycoprotein bound with glucose (2 mol/mol of protein) and stabilized by 2 atoms of each of Ca²⁺ and Mn²⁺ per molecule of protein. It highly agglutinated human, rabbit and rat erythrocytes but weakly agglutinated chicken erythrocytes, while no agglutination occurred with sheep erythrocytes. Hemagglutination was markedly affected by acidic pH, but was heat stable below 60 °C for 30 min. Among the various tested sugars, PSL agglutination was most inhibited by mannose. PSL is rich in hydroxyl amino acids while totally lacking sulfur amino acids. PSL inhibited the growth of Aspergillus flavus, Trichoderma viride and Fusarium oxysporum.     

An "offshore-resistant" degree program

The new University of Guelph-Humber has created an innovative, combined university degree and college diploma program in computer science and communications technology. This venture between the university and the Humber Institute of Technology and advanced learning offers jointly an honors baccalaureate in computing science from Guelph and a diploma in wireless and telecommunications systems technology from Humber. The program, one of six joint programs between the two institutions, specializes in distributed and wireless computing.     

Controlled-Release Frusemide Microcapsules: Preformulation Studies

Microencapsulation of plain frusemide or its solid-dispersion with PEG 6000 was achieved by phase-separation coacervation. Formulations showed reasonable in-vitro dissolution behaviour were assessed for their absorption rates by LD₅₀ testing in mice. Toxicity studies showed close agreement between the increase in lethal dose and the decrease in dissolution rate and revealed that the formulation containing frusemide as fused mixture with PEG 6000 and microencapsulated with polystyrene, in frusemide-PEG 6000-polystyrene weight ratio of 2:2:1, was the formula of choice for prolonging the absorption, hence, the action of frusemide.     

Work flow methodology : A proposed approach for analysis and conceptual design of large scale computer based information systems

The objective of this paper is to explain our approach called “Work Flow Methodology for Analysis and Conceptual Data Base Design of Large Scale Computer Based Information System”. The user fills in, through the different steps of the methodology and in the light of the definition of dynamic adaptive system, a number of forms which relate the topological dimension to the time dimension for each application of a given system. In addition, we obtain the “Unit Subschema” which defines the responsibilities of issuing and authorization of receiving information at the proper time. Finally, we apply our methodology to the Registration System in Kuwait University.     

Modal analysis of open groove waveguide

A self-contained analysis for lower-order modes in the open groove waveguide is presented. By adopting an approximate form for the fields in the outer region of the guide and then performing the remainder of the analysis rigorously, closed-form results for the fields and modal equation are obtained. The analysis shows a simple transverse network representation that can be compared with that obtained by A.A. Oliner and P. Lampariello (ibid., vol.33, no.9, p.755-64, 1985). Universal dispersion curves, obtained numerically for the dominant mode, are presented     

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.     

Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for Clustered IoT Driven Ubiquitous Computing System

In the Internet of Things (IoT) based system, the multi-level client’s requirements can be fulfilled by incorporating communication technologies with distributed homogeneous networks called ubiquitous computing systems (UCS). The UCS necessitates heterogeneity, management level, and data transmission for distributed users. Simultaneously, security remains a major issue in the IoT-driven UCS. Besides, energy-limited IoT devices need an effective clustering strategy for optimal energy utilization. The recent developments of explainable artificial intelligence (XAI) concepts can be employed to effectively design intrusion detection systems (IDS) for accomplishing security in UCS. In this view, this study designs a novel Blockchain with Explainable Artificial Intelligence Driven Intrusion Detection for IoT Driven Ubiquitous Computing System (BXAI-IDCUCS) model. The major intention of the BXAI-IDCUCS model is to accomplish energy efficacy and security in the IoT environment. The BXAI-IDCUCS model initially clusters the IoT nodes using an energy-aware duck swarm optimization (EADSO) algorithm to accomplish this. Besides, deep neural network (DNN) is employed for detecting and classifying intrusions in the IoT network. Lastly, blockchain technology is exploited for secure inter-cluster data transmission processes. To ensure the productive performance of the BXAI-IDCUCS model, a comprehensive experimentation study is applied, and the outcomes are assessed under different aspects. The comparison study emphasized the superiority of the BXAI-IDCUCS model over the current state-of-the-art approaches with a packet delivery ratio of 99.29%, a packet loss rate of 0.71%, a throughput of 92.95 Mbps, energy consumption of 0.0891 mJ, a lifetime of 3529 rounds, and accuracy of 99.38%.     

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks (CRWSN) can be defined as a promising technology for developing bandwidth-limited applications. CRWSN is widely utilized by future Internet of Things (IoT) applications. Since a promising technology, Cognitive Radio (CR) can be modelled to alleviate the spectrum scarcity issue. Generally, CRWSN has cognitive radio-enabled sensor nodes (SNs), which are energy limited. Hierarchical cluster-related techniques for overall network management can be suitable for the scalability and stability of the network. This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering (MDMO-EAC) Scheme for CRWSN. The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN. Besides, the MDMO-EAC algorithm is based on the dwarf mongoose optimization (DMO) algorithm design with oppositional-based learning (OBL) concept for the clustering process, showing the novelty of the work. In addition, the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency. The presented model is validated using a comprehensive range of experiments, and the outcomes were scrutinized in varying measures. The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.