Optimizing the Roadside Unit Deployment Mechanism in VANET with Efficient Protocol to Prevent Data Loss

Wireless Personal Communications - RTS/ CTS protocol serves multihop wireless networks poorly due to its single-hop design. TDMA protocol surpasses RTS/ CTS but unable to solve the dynamic needs of...     

Feature selection techniques in the context of big data: taxonomy and analysis

Applied Intelligence - Recent advancements in Information Technology (IT) have engendered the rapid production of big data, as enormous volumes of data with high dimensional features grow...     

Magnesium-doped ZnO nanorod electrolyte–insulator–semiconductor (EIS) sensor for detecting calcium ions

Electrolyte-Membrane-Insulator-Semiconductor (EMIS) sensors based on ZnO undoped and doped with a different molar ratio of Mg/Zn are demonstrated to detect calcium ions. The samples grown on the silicon substrates by the hydrothermal method were characterized to explore the impact of Mg content on the structural and compositional characteristics and sensing performance by X-ray diffraction analysis, field emission scanning electron microscopy, and X-ray photoelectron spectroscopy. The results indicated that the EIMS based on ZnO nanorods doped with 3% Mg had a high Ca²⁺ ion sensitivity (69 mV/decade) and linearity (99.8%). In addition, the samples have good stability with a low drift rate of 0.398 mV/h and possess great selectivity over other interference ions such as Na⁺, K⁺, Mg²⁺, and Ni²⁺ due to the employment of an ionophore membrane.

     

A smart intuitionistic fuzzy-based framework for round-robin short-term scheduler

The Journal of Supercomputing - A smart intuitionistic fuzzy-based framework is designed to facilitate adaptability by providing continuous changes in the size of time slice to scheduler at run...     

Energy-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output (MIMO) network in which each base station (BS) is equipped with a large number of antennas and each base station (BS) adapts the number of antennas to the daily load profile (DLP). This paper takes into consideration user location distribution (ULD) variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO system. ULD variation is modeled by dividing the cell into two coverage areas with different user densities: boundary focused (BF) and center focused (CF) ULD. All cells are assumed identical in terms of BS configurations, cell loading, and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak load. Together with energy efficiency (EE) we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size, available bandwidth, output power level of the BS, and maximum output power of the power amplifier (PA) at different cell loading. We also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business, residential, street, and highway areas.     

Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Content authentication, integrity verification, and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology. In this paper, a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking (SFASCDW) is proposed for content authentication and tampering detection of English text. A first-level order of alphanumeric mechanism, based on hidden Markov model, is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach. The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text. Moreover, he extracts the features of the interrelationship among the contexts of the text, utilizes the extracted features as watermark information, and validates it later with the studied English text to detect any tampering. SFASCDW has been implemented using PHP with VS code IDE. The robustness, effectiveness, and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks, namely insertion, reorder, and deletion. The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.     

Forecasting Mental Stress Using Machine Learning Algorithms

Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is divided into six parts: Proposed Research Architecture (PRA), Data Pre-processing Approach (DPA), Research Hypothesis Testing (RHT), Concentrated Algorithm Pipeline (CAP), Loss Optimization Stratagem (LOS), and Model Deployment Architecture (MDA). The Null Hypothesis and Alternative Hypothesis are applied to test the RHT. In addition, Ensemble Learning Approach (ELA) and Frequent Model Retraining (FMR) have been utilized for optimizing the loss function. Besides, the Features Importance Interpretation is also delineated in this research. These forecasts could help individuals connect with expert mental health specialists more quickly and easily. According to the findings, 71% of people with depression and 80% of those who do not have depression can be appropriately diagnosed. This study obtained 91% and 92% accuracy through the Random Forest (RF) and Extra Tree Classifier. But after applying the Receiver operating characteristic (ROC) curve, 79% accuracy was found on top of RF, 81% found on Extra Tree, and 82% recorded for the eXtreme Gradient Boosting (XGBoost) algorithm. Besides, several factors are identified in terms of predicting depression through statistical data analysis. Though the additional effort is needed to develop a more accurate model, this model can be adjustable in the healthcare sector for diagnosing depression.     

Tunable microwave absorption performance of carbon fiber-reinforced reaction bonded silicon nitride composites

The hybrid network of Si₃N₄ whiskers and conducting carbon fiber has great potential for microwave absoprtion applications. The high electrical conductivity of the carbon fiber helps to transform the microwave transparent Si₃N₄ into microwave absorbing materials. Herein, the microwave absorption performance of 5–20 vol % of carbon fiber reinforced reaction bonded Si₃N₄ (C_f-RBSN) composites have been discussed in detail. The C_f reinforcement tuned the X-band dielectric properties of the RBSN composites. The 5 vol % C_f-RBSN composite exhibit a minimum reflection loss (RL_min) of ?36.16 dB (99.998% microwave absorption) at 11.89 GHz and a high specific reflection loss of 920 dB. g^?1 for 5.9 mm thickness, while 20 vol % C_f-RBSN composites resulted in RL_min of ?22.86 dB at 11.56 GHz with a low thickness of 1.5 mm. Thus, the superior microwave absorption performance of the prepared lightweight composites results from the multiple interfacial polarization, dipole polarization, and conduction loss due to the 3D network of interconnected Si₃N₄ whiskers and C_f.     

Battery Recovery-Aware Optimization for Embedded System Communications

Wireless Personal Communications - In this paper, we consider a point-to-point wireless communications for embedded battery powered systems. We aim to provide an optimal use of all the usable...     

High-efficient multifunctional self-heating nanocomposite-based MWCNTs for energy applications

Self-heating of nanocomposite materials based on the joule heating effect is suitable for numerous engineering applications. In this study, a high-efficiency self-heating nanocomposite, using high conductive multi-walled carbon nanotubes (MWCNTs)-based phenolic resin, was fabricated with a hot press method. The microstructure and the thermal stability of self-heating nanocomposite were studied by X-ray diffraction, scanning electronic microscopy, and thermogravimetric tests. Electromechanical and thermal performance tests were conducted to investigate their potential as a self-heating application. Results showed that the compressive strength, modulus, and the piezo-resistive behaviour were higher after adding MWCNTs to the phenolic resin, indicating better load transfer and self-damage sensing as well. Moreover, at 4.0 wt% of MWCNTs concentration, the electrical conductivity of a self-heating nanocomposite showed a higher value of 13.26 S/m which was also found to be proportionally increased with the thickness of the samples, it was ≈25.5 and ≈12.8 S/m for 10 and 3 mm, respectively. In addition, a steady-state temperature of ≈110°C could be reached at low applied volts (8 V) as well as its heating performance was significantly dependent on the input power and the thickness of the sample. This is also confirmed by statistical results between the sample with thicknesses of 3 and 10 mm in terms of power consumption with P value ≈ .0001. Furthermore, the influence of Joule heating was estimated analytically based on the one-dimensional heat transfer equation in companying with other previous models. The estimated distributed temperatures values were in good agreement with the experimental results. The self-heating nanocomposite described in this study has the potential to be used in various industrial applications and a wide range of sectors due to its ability to self-damage sensing, easy fabrication, and high heating efficiency at low power consumption.