Modelling of air damping effect on the performance of encapsulated MEMS resonators

Microsystem Technologies - The dynamic performance of a micro-resonator depends on its energy loss mechanism which is quantified by Q-factor (Quality factor). This paper presents numerical...     

Intrusion Detection Systems Using Blockchain Technology: A Review,Issues and Challenges

Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches. However, the current approaches have challenges in detecting intrusions, which may affect the performance of the overall detection system as well as network performance. For the time being, one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter how far apart they are. Recently, blockchain was integrated into intrusion detection systems to enhance their overall performance. Blockchain has also been adopted in healthcare, supply chain management, and the Internet of Things. Blockchain uses robust cryptography with private and public keys, and it has numerous properties that have leveraged security’s performance over peer-to-peer networks without the need for a third party. To explore and highlight the importance of integrating blockchain with intrusion detection systems, this paper provides a comprehensive background of intrusion detection systems and blockchain technology. Furthermore, a comprehensive review of emerging intrusion detection systems based on blockchain technology is presented. Finally, this paper suggests important future research directions and trending topics in intrusion detection systems based on blockchain technology.     

Novel method to transform ZnO nanorods into interlaced tattered nanosheets by changing the target inclination angle

ZnO nanoparticles were synthesized by liquid-phase pulse laser ablation of a Zn foil target immersed in deionized water. Nanosecond Q-switched Nd:YAG laser pulses of 532 nm were applied to the Zn foil target at a perpendicular and inclined (θ = 45°) angles. X-ray diffraction analysis revealed that both cases feature a ZnO nanostructure with a hexagonal wurtzite structure and that the particle size increases with the inclined target angle. Field emission scanning electron microscopy results of a colloidal drop cast on a glass substrate showed the ZnO has a nanorod structure in the case of a perpendicular target angle and an interlaced tattered nanosheet structure in the case of an inclined target angle. Photoluminescence spectra showed emission peaks in the UV, violet, blue, and green spectral regions, which correspond to excitonic and various defects resulting in an enhancement of emissions at inclined target angle.     

Linear and nonlinear viscoelastic and viscoplastic analysis of asphalt binders with warm mix asphalt additives

Warm-Mix Asphalt (WMA) is a widely used product, which proved a contribution to the reduction in asphalt mixing and compaction temperatures. This reduction leads to lower fuel consumption and smoke emission in asphalt plants. Most of the characterisation of binders used in WMA has focused in the past on measuring linear viscoelastic properties and associated Superpave parameters. Several studies have shown that the average stresses and strains of the asphalt mixture remain mostly within the linear viscoelastic response. However, localised strains in the binder phase of the mixture could reach values high enough to induce nonlinear viscoelastic and viscoplastic deformations. Therefore, this study focuses on an experimental and analytical evaluation of linear, nonlinear viscoelastic and viscoplastic responses of selected binders modified for use in WMA. The first part of the paper analyses the linear viscoelastic material properties and their ability to evaluate permanent deformation resistance. Then, the non-recoverable creep compliance parameter obtained from the Multiple Stress Creep Recovery (MSCR) test is analysed to assess the nonlinear response and permanent deformation of asphalt binders. The paper utilises a nonlinear plasto-viscoelastic (NPVE) approach to assess and quantify the nonlinear plasto-viscoelastic response of binders by separating the recoverable and irrecoverable strains measured in the MSCR test. Two WMA additives were included in this study by mixing them with polymer-modified and unmodified asphalt binders. Analysis of results showed that the NPVE approach captured a higher percentage of recovery than the NLVE approach. However, binder’s performance evaluation and ranking did not change by adopting the NPVE approach. The nonlinear viscoelastic parameters provided insight on the behaviour of asphalt binders mixed with WMA additives during loading cycles. Sasobit showed higher influence than Advera on binders in resisting permanent deformation by increasing the recoverable strain during the unloading phase.     

Electrical and Mechanical Behavior of Aerosol Jet–Printed Gold on Alumina Substrate for High-Temperature Applications

There is a growing interest in the development of microelectronics that can perform reliably and robustly at temperatures above 300 °C. Such devices require stable thermal properties, low thermal drift, and thermal cycling resistance. Conventional hybrid circuit technology demonstrates high-temperature packages, but the high costs and lead time are significant drawbacks. In contrast, additive manufacturing processes, including aerosol jet printing (AJP), offer cost and time benefits, as well as 3D structures and embedded features. However, the properties and reliability of additive packaging materials at extreme temperatures are not well known. Herein, the reliability at temperatures up to 750 °C in terms of electrical performance and mechanical strength of aerosol jet printed gold thick films onto ceramic substrates are assessed. Thermal coefficient of resistance of printed gold films is measured. The electrical resistance stability and leakage current of printed gold structures are also characterized during over 100 h of aging at temperatures up to 750 °C. Finally, the mechanical adhesion strength of the printed gold films is evaluated after aging for 100 h at temperatures up to 750 °C. The adhesion of the printed gold to the ceramic substrates remains high after aging, very stable resistances and minimal leakage currents have been observed.     

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.