Design and optimization of buffer chains and logic circuits in aBiCMOS environment

The design and optimization of BiCMOS buffer chains and multi level logic circuits are reported. BiCMOS speedup contours are introduced and analytical expressions for the delay are obtained. The speedup contours and the delay expressions were used in the design and optimization of BiCMOS buffer chains. Also, general design guidelines, which can be easily automated, for circuit design in a BiCMOS environment are given. Designing multistage mixed CMOS/BiCMOS buffers, BiCMOS complex logic gates, and multi level CML (current mode logic) gates is also studied     

Phase-domain fractional-N frequency synthesizers

The concept of phase-domain fractional-N frequency synthesis is presented. Synthesizers using this architecture can achieve fast frequency switching without limiting the minimum channel spacing. In this architecture, a numerical phase comparator is used in conjunction with weighting coefficients, as a linear weighted phase-frequency detector. The synthesizer output spur level is determined by two factors. Namely, the delay of the numerical phase comparator, and the accuracy of the digital-to-analog convertor (DAC) used to convert the phase error to the analog domain. A novel second-order timing-error cancelation scheme is proposed to eliminate the effect of the phase comparator delays. Using this technique together with a 10-bit accuracy DAC, a maximum spur level of less than -65 dBc is simulated for a 900-MHz synthesizer. The settling time of the simulated synthesizer is less than 7 /spl mu/s, and is independent of the channel spacing. The details of the synthesizer architecture, design considerations, and system-level simulations are presented. Implementation issues including the DAC accuracy and timing-error effects are discussed extensively throughout the text.     

Editorial

Higher-Order and Symbolic Computation -     

Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

This paper proposes two novel packet scheduling schemes, called as throughput enhanced scheduling (TES) and TES plus (TES+), for future ultra‐dense networks. These schemes introduce two novel parameters to the scheduling decision making and reformulate the parameters used by the state‐of‐the‐art schemes. The aim is to have a more balanced weight distribution between delay and throughput‐related parameters at scheduling decisions. Also include a new telecommunications related parameter into scheduling decision making that has not been studied by popular schedulers. The performance of novel schemes is compared with well‐known schemes—proportional fairness (PF), exponential/proportional fairness (EXP/PF), and M‐LWDF. For performance evaluation, five performance metrics—average spectral efficiency and delay, quality of service (QoS) violation ratio, jitter, and Jain's fairness index—are investigated. The simulation results show that proposed schemes can outperform all the compared scheduling schemes.     

DORS: A data overhead reduction scheme for hybrid networks in smart cities

Today, smart cities represent an effective digital platform for facilitating our lives by shifting all stakeholders toward more sustainable behavior. Consequently, the field of smart cities has become an increasingly important research area. The smart city comprises a huge number of hybrid networks, with each network containing an enormous number of nodes that transmit massive amounts of data, thus giving rise to many network problems, such as delay and loss of connectivity. Decreasing the amount of such transmitted data is a great challenge. This paper presents a data overhead reduction scheme (DORS) for heterogeneous networks in smart city environments that comprise five different methods: median, nonlinear least squares, compression, data merging, and prioritization. Each method is applied according to the current status of quality of service. To measure the performance of the proposed model, a simulation environment is constructed for a smart city using network simulation package, NS2. The obtained results indicate that DORS has the capability to decrease the size of transmitted data in the simulated smart city environment while attaining a notable performance enhancement in terms of data reduction rate, end‐to‐end delay, packet loss ratio, throughput, and energy consumption ratio.     

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

This article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.     

Highly Conductive,Scalable, and Machine Washable Graphene‐Based E‐Textiles for Multifunctional Wearable Electronic Applications

Graphene‐based textiles show promise for next‐generation wearable electronic applications due to their advantages over metal‐based technologies. However, current reduced graphene oxide (rGO)‐based electronic textiles (e‐textiles) suffer from poor electrical conductivity and higher power consumption. Here, highly conductive, ultraflexible, and machine washable graphene‐based wearable e‐textiles are reported. A simple and scalable pad?dry?cure method with subsequent roller compression and a fine encapsulation of graphene flakes is used. The graphene‐based wearable e‐textiles thus produced provide lowest sheet resistance (≈11.9 Ω sq^?1) ever reported on graphene e‐textiles, and highly conductive even after 10 home laundry washing cycles. Moreover, it exhibits extremely high flexibility, bendability, and compressibility as it shows repeatable response in both forward and backward directions before and after home laundry washing cycles. The scalability and multifunctional applications of such highly conductive graphene‐based wearable e‐textiles are demonstrated as ultraflexible supercapacitor and skin‐mounted strain sensors.     

Empirical study on multiclass classification‐based network intrusion detection

Early and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short‐term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well‐known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL‐KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach.     

Porcine skin gelatin–silver nanocomposites: synthesis,characterisation, cell cytotoxicity,and antibacterial properties

Silver nanoparticles (AgNPs) were synthesised with hydrothermal autoclaving technique by using AgNO₃ salt (silver precursor) at different concentrations (0.01, 0.1, 0.55, 1.1, 5.5, and 11 mM) and porcine skin (1% (w/v)) gelatin polymeric matrix (reducing and stabiliser agent). The reaction was performed in an autoclave at 103 kPa and 121°C and the hydrothermal autoclaving exposure time and AgNO₃ molar concentration were varied at a constant porcine skin gelatin concentration. The as‐prepared AgNPs were characterised by UV–visible spectroscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy. The antibacterial properties of AgNPs were tested against gram‐positive and gram‐negative bacteria. Furthermore, 3‐(4,5‐dimethylthiazol‐2‐yl) 2,5‐diphenyltetrazolium bromide and 2,2‐diphenyl‐1‐picrylhydrazyl assays were used to test whether the synthesised AgNPs can be potentially applied in cancer therapy or used as an antioxidant. This approach is a promising simple route for synthesising AgNPs with a smaller average particle 10 nm diameter. Furthermore, AgNPs exhibited a good cytotoxicity activity, reducing the viability of the liver cancer cell line HepG2 with a moderate IC₅₀; they also showed a low‐to‐fair antioxidant activity. In addition, AgNPs had a remarkable preferential antibacterial activity against gram‐positive bacteria than gram‐negative bacteria. Therefore, these fabricated AgNPs can be used as an antibacterial agent in curative and preventive health care.Inspec keywords: gelatin, silver, nanoparticles, nanocomposites, nanobiotechnology, biomedical materials, antibacterial activity, microorganisms, Fourier transform infrared spectra, ultraviolet spectra, visible spectra, transmission electron microscopy, cancer, cellular biophysicsOther keywords: porcine skin gelatin–silver nanocomposites, cell cytotoxicity, antibacterial properties, silver nanoparticles, hydrothermal autoclaving technique, gelatin polymeric matrix, UV–visible spectroscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, gram‐positive bacteria, gram‐negative bacteria, 3‐(4,5‐dimethylthiazol‐2‐yl) 2,5‐diphenyltetrazolium bromide assays, 2,2‐diphenyl‐1‐picrylhydrazyl assays, cancer therapy, antioxidant, liver cancer cell line HepG2, Ag     

Image denoising algorithm based on the convolution of fractional Tsallis entropy with the Riesz fractional derivative

Image denoising is an important component of image processing. The interest in the use of Riesz fractional order derivative has been rapidly growing for image processing recently. This paper mainly introduces the concept of fractional calculus and proposes a new mathematical model in using the convolution of fractional Tsallis entropy with the Riesz fractional derivative for image denoising. The structures of n × n fractional mask windows in the x and y directions of this algorithm are constructed. The image denoising performance is assessed using the visual perception, and the objective image quality metrics, such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The proposed algorithm achieved average PSNR of 28.92 dB and SSIM of 0.8041. The experimental results prove that the improvements achieved are compatible with other standard image smoothing filters (Gaussian, Kuan, and Homomorphic Wiener).