Optimum range of angle tracking radars: a theoretical computing

In this paper, we determine an optimal range for angle tracking radars (ATRs) based on evaluating the standard deviation of all kinds of errors in a tracking system. In the past, this optimal range has often been computed by the simulation of the total error components; however, we are going to introduce a closed form for this computation which allows us to obtain the optimal range directly. Thus, for this purpose, we firstly solve an optimization problem to achieve the closed form of the optimal range (Ropt.) and then, we compute it by doing a simple simulation. The results show that both theoretical and simulation-based computations are similar to each other

Evaluation of the effect of hypericum triquetrifolium turra on memory impairment induced by chronic psychosocial stress in rats: Role of BDNF

Background: Chronic psychosocial stress impairs memory function and leads to a depression-like phenotype induced by a persistent status of oxidative stress. Hypericum perforatum L. (St. John’s wort) is widely used to relieve symptoms of anxiety and depression; however, its long-term use is associated with adverse effects. Hypericum triquetrifolium Turra is closely related to H. perforatum. Both plants belong to Hypericaceae family and share many biologically active compounds. Previous work by our group showed that methanolic extracts of H. triquetrifolium have potent antioxidant activity as well as high hypericin content, a component that proved to have stress-relieving and antidepressant effects by other studies. Therefore, we hypothesized that H. triquetrifolium would reduce stress-induced cognitive impairment in a rat model of chronic stress. Objective: To determine whether chronic treatment with H. triquetrifolium protects against stress-associated memory deficits and to investigate a possible mechanism. Methods: The radial arm water maze (RAWM) was used to test learning and memory in rats exposed to daily stress using the resident-intruder paradigm. Stressed and unstressed rats received chronic H. triquetrifolium or vehicle. We also measured levels of brain-derived neurotrophic factor (BDNF) in the hippocampus, cortex and cerebellum. Results: Neither chronic stress nor chronic H. triquetrifolium administration affected performance during acquisition. However, memory tests in the RAWM showed that chronic stress impaired different post-encoding memory stages. H. triquetrifolium prevented this impairment. Furthermore, hippocampal BDNF levels were markedly lower in stressed animals than in unstressed animals, and chronic administration of H triquetrifolium chronic administration protected against this reduction. No significant difference was observed in the effects of chronic stress and/or H. triquetrifolium treatment on BDNF levels in the cerebellum and cortex. Conclusion: H. triquetrifolium extract can oppose stress-associated hippocampus-dependent memory deficits in a mechanism that may involve BDNF in the hippocampus

Forward error correction based on algebraic-geometric theory

This book covers the design, construction, and implementation of algebraic-geometric codes from Hermitian curves. Matlab simulations of algebraic-geometric codes and Reed-Solomon codes compare their bit error rate using different modulation schemes over additive white Gaussian noise channel model. Simulation results of Algebraic-geometric codes bit error rate performance using quadrature amplitude modulation (16QAM and 64QAM) are presented for the first time and shown to outperform Reed-Solomon codes at various code rates and channel models. The book proposes algebraic-geometric block turbo codes. It also presents simulation results that show an improved bit error rate performance at the cost of high system complexity due to using algebraic-geometric codes and Chase-Pyndiah’s algorithm simultaneously. The book proposes algebraic-geometric irregular block turbo codes (AG-IBTC) to reduce system complexity. Simulation results for AG-IBTCs are presented for the first time

Optimized Machine Learning-Based Intrusion Detection System for Fog and Edge Computing Environment

As a new paradigm, fog computing (FC) has several characteristics that set it apart from the cloud computing (CC) environment. Fog nodes and edge computing (EC) hosts have limited resources, exposing them to cyberattacks while processing large streams and sending them directly to the cloud. Intrusion detection systems (IDS) can be used to protect against cyberattacks in FC and EC environments, while the large-dimensional features in networking data make processing the massive amount of data difficult, causing lower intrusion detection efficiency. Feature selection is typically used to alleviate the curse of dimensionality and has no discernible effect on classification outcomes. This is the first study to present an Effective Seeker Optimization model in conjunction with a Machine Learning-Enabled Intrusion Detection System (ESOML-IDS) model for the FC and EC environments. The ESOML-IDS model primarily designs a new ESO-based feature selection (FS) approach to choose an optimal subset of features to identify the occurrence of intrusions in the FC and EC environment. We also applied a comprehensive learning particle swarm optimization (CLPSO) with Denoising Autoencoder (DAE) for the detection of intrusions. The development of the ESO algorithm for feature subset selection and the DAE algorithm for parameter optimization results in improved detection efficiency and effectiveness. The experimental results demonstrated the improved outcomes of the ESOML-IDS model over recent approaches

Optimized Machine Learning-Based Intrusion Detection System for Fog and Edge Computing Environment

As a new paradigm, fog computing (FC) has several characteristics that set it apart from the cloud computing (CC) environment. Fog nodes and edge computing (EC) hosts have limited resources, exposing them to cyberattacks while processing large streams and sending them directly to the cloud. Intrusion detection systems (IDS) can be used to protect against cyberattacks in FC and EC environments, while the large-dimensional features in networking data make processing the massive amount of data difficult, causing lower intrusion detection efficiency. Feature selection is typically used to alleviate the curse of dimensionality and has no discernible effect on classification outcomes. This is the first study to present an Effective Seeker Optimization model in conjunction with a Machine Learning-Enabled Intrusion Detection System (ESOML-IDS) model for the FC and EC environments. The ESOML-IDS model primarily designs a new ESO-based feature selection (FS) approach to choose an optimal subset of features to identify the occurrence of intrusions in the FC and EC environment. We also applied a comprehensive learning particle swarm optimization (CLPSO) with Denoising Autoencoder (DAE) for the detection of intrusions. The development of the ESO algorithm for feature subset selection and the DAE algorithm for parameter optimization results in improved detection efficiency and effectiveness. The experimental results demonstrated the improved outcomes of the ESOML-IDS model over recent approaches