Recent advances in isotropic conductive adhesives for electronics packaging applications

Isotropic conductive adhesives (ICAs) have recently received a lot of focus and attention from the researchers in electronics industry as a potential substitute to lead-bearing solders. Numerous studies have shown that ICAs possess many advantages over conventional soldering such as environmental friendliness, finer pitch printing, lower temperature processing and more flexible and simpler processing. However, complete replacement of soldering by ICAs is yet not possible owing to several limitations of ICAs which are mainly related to reliability aspects like limited impact resistance, unstable contact resistance, low adhesion and conductivity etc. Continued efforts for last 15 years have resulted in development of ICAs with improved properties. This review article is aimed at providing a better understanding of ICAs, their principles, performance and significant research and development work addressing the technological utility of ICAs.     

A new technique for the construction of confusion component based on inverse LA-semigroups and its application in steganography

The present article introduced a novel idea for information hiding namely steganography. We have used new notions for the construction of the nonlinear component for block cipher based on inverse LA-semigroups. This nonlinear component fundamentally provides confidentiality in the proposed steganographic algorithm. The construction of the algorithm is fundamentally twofold. Firstly, we have constructed a novel scheme to design confusion component namely substitution box (S-box). Secondly, we have utilized the anticipated nonlinear component in digital steganography. The suggested algorithm is tested for different standard digital images. The authentication of the proposed algorithm is confirmed through statistical analysis.

     

On ℤ2ℤ2[u]-additive codes

In this paper, a new class of additive codes which is referred to as ?₂ ?₂[u]-additive codes is introduced. This is a generalization towards another direction of recently introduced ?₂ ?₄-additive codes [J. Borges, C. Fernández-Córdoba, J. Pujol, J. Rif´a, and M. Villanueva, ?₂ ?₄-linear codes: Generator matrices and duality, Designs Codes Cryptogr. 54(2) (2010), pp. 167–179]. ?₂ ?₄-additive codes have shown to provide a promising class of codes with their algebraic structure and applications such as steganography. The standard generator matrices are established and by introducing orthogonality the parity-check matrices are also obtained. A MacWilliams-type identity that relates the weight enumerator of a code with its dual is proved. Furthermore, a Gray map that maps these codes to binary codes is defined and some examples of optimal codes which are the binary Gray images of ?₂ ?₂[u]-additive codes are presented.     

Three-qubit Grover’s algorithm using superconducting quantum interference devices in cavity-QED

We present a scheme for the implementation of three-qubit Grover’s algorithm using four-level superconducting quantum interference devices (SQUIDs) coupled to a superconducting resonator. The scheme is based on resonant, off-resonant interaction of the cavity field with SQUIDs and application of classical microwave pulses. We show that adjustment of SQUID level spacings during the gate operations, adiabatic passage, and second-order detuning are not required that leads to faster implementation. We also show that the marked state can be searched with high fidelity even in the presence of unwanted off-resonant interactions, level decay, and cavity dissipation.     

Performance analysis of multimedia based web traffic with QoS constraints

During the recent years, there has been a tremendous growth in the development and deployment of multimedia based networked applications such as video streaming, IP telephony, interactive games, among others. These applications, in contrast to elastic applications such as email and data sharing, are delay and delay jitter sensitive but can tolerate certain level of packet loss. A vital element of end-to-end delay and delay jitter is the random queueing delays in network switches and routers. Analysis of robust mechanisms for buffer management at network routers needs to be carried out in order to reduce end-to-end delay for traffic generated by multimedia applications. In this context, a threshold based buffer management scheme for accommodating multiple class multimedia traffic in network routers has been analysed. This technique effectively controls the allocation of buffer to various traffic classes according to their delay constraints. The forms of the joint state probabilities, as well as basic performance measures such as blocking probabilities are analytically established at equilibrium. Typical numerical experiments are included to illustrate the credibility of the proposed mechanism in the context of different quality of service (QoS) grades for various network traffic classes. This model, therefore, can be used as a powerful tool to provide a required grade of service to a particular class of multimedia based web traffic in any heterogeneous network.     

Potentiometric urea biosensor based on BSA embedded surface modified polypyrrole film

A potentiometric biosensor based on bovine serum albumin (BSA) embedded surface modified polypyrrole has been developed for the quantitative estimation of urea in aqueous solution. The enzyme, urease (Urs), was covalently linked to free amino groups present over the BSA embedded modified surface of the conducting polypyrrole film electrochemically deposited onto an indium–tin-oxide (ITO) coated glass plate. The biosensor has been characterized by UV–visible, infrared spectroscopy and SEM. Potentiometric and spectrophotometric response of the enzyme electrode (Urs/BSA-PPy/ITO) were measured as a function of urea concentration in Tris–HCl buffer (pH 7.0). It has been found that the electrode responds to low urea concentration with wider range of detection. The electrode showed a linear response range of 6.6 × 10⁻⁶ to 7.5 × 10⁻⁴ M urea. The response time is about 70–90 s reaching to a 95% steady-state potential value and 75% of the enzyme activity is retained for about 2 months. These results indicate an efficient covalent linkage of enzyme to free amino groups of the BSA molecules over the surface of polypyrrole film, which leads to high enzyme loading, an increased lifetime stability of the electrode and an improved wide range of detection of low urea concentration in aqueous solution.     

Improved aquila optimization-based parameter evaluation framework and meta-heuristic algorithm in radio-over-fiber SpatialMux multi-input multi-output long-term evolution system

In heterogeneous access network, Multiple-Input Multiple-Output (MIMO) radio-over-fiber (RoF) system is an efficient approach for multiple signal transmission with low cost and complexity. The performance of RoF fronthaul system in MIMO system will be varied with different nonlinear effects. By adjusting various transmission parameters, such as the input signal power or the laser bias current, the nonlinear impacts produced by the RoF system can be reduced. In this paper, a novel algorithm Improved Aquila Optimization (IAO) is proposed to optimize transmission circumstances of MIMO RoF system. It determines the appropriate bias current for both lasers and Radio Frequency (RF) signal power in a short period. The input signals are wavelength multiplexed with Intensity Modulation and Direct Detection (IM/DD) applied. The carrier as well as transmission frequency is governed by the MIMO-Long-Term Evolution (LTE) standard. The proposed system is implemented in MATLAB, and the performance is evaluated. The experimental results show that fast convergence and trade-off between noise and nonlinearity are obtained with varying bandwidth. In the experimental scenario, the maximum Error Vector Magnitude (EVM) of 1.88, 3.14, and signal-to-noise ratio (SNR) of 3.204, and 2.698 was attained for both quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) modulation. [Correction added on 24 April 2023, after first online publication: the SNR values were corrected in the preceding sentence.] For 100 iterations, the processing time was reduced to 0.137 s. When compared with the conventional state-of-the-art approaches, the accuracy and computational complexity of the proposed approach are improved.     

A Hybrid Reconfigurability Structure and Improved Gain Characteristics for a Novel 5G Monopole Antenna for Future Mobile Communication

Wireless Personal Communications - Mobile edge computation (MEC) is a potential technology to reduce the energy consumption and task execution delay for tackling computation-intensive tasks on...     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.