MIMO iterative receiver SNR estimation strategies for link to system interfacing

This paper presents link to system (L2S) interfacing technique for multiple input and multiple output (MIMO) iterative receivers. In L2S interfacing, usually the post detection signal to noise ratio (SNR)‐based frame error rate lookup tables (LUT) are used to predict the link level performance of receivers. While L2S interfacing for linear MIMO receivers can be conveniently implemented, it is more challenging for MIMO iterative receivers due to unavailability of the closed form SNR expressions. In this paper, we propose three methods for post detection SNR estimation for MIMO iterative receivers. The first is based on the QR decomposition of the channel matrix, the second relies on the residual noise calculation based on the soft symbols, and the third exploits the closed form SNR expressions for linear receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. It is shown that linear and residual noise‐based SNR approximations result in a very good prediction performance whereas the performance of QR decomposition‐based method degrades for higher order modulations and coding schemes. This paper presents link to system interfacing technique for MIMO iterative receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. Three post detection SNR evaluation schemes have been proposed for link to system interfacing all of which give good prediction performance especially at lower order modulation.     

Colloidal Nanosurfactants for 3D Conformal Printing of 2D van der Waals Materials

Printing techniques using nanomaterials have emerged as a versatile tool for fast prototyping and potentially large-scale manufacturing of functional devices. Surfactants play a significant role in many printing processes due to their ability to reduce interfacial tension between ink solvents and nanoparticles and thus improve ink colloidal stability. Here, a colloidal graphene quantum dot (GQD)-based nanosurfactant is reported to stabilize various types of 2D materials in aqueous inks. In particular, a graphene ink with superior colloidal stability is demonstrated by GQD nanosurfactants via the π–π stacking interaction, leading to the printing of multiple high-resolution patterns on various substrates using a single printing pass. It is found that nanosurfactants can significantly improve the mechanical stability of the printed graphene films compared with those of conventional molecular surfactant, as evidenced by 100 taping, 100 scratching, and 1000 bending cycles. Additionally, the printed composite film exhibits improved photoconductance using UV light with 400 nm wavelength, arising from excitation across the nanosurfactant bandgap. Taking advantage of the 3D conformal aerosol jet printing technique, a series of UV sensors of heterogeneous structures are directly printed on 2D flat and 3D spherical substrates, demonstrating the potential of manufacturing geometrically versatile devices based on nanosurfactant inks.     

COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

The purpose of this research is the segmentation of lungs computed tomography (CT) scan for the diagnosis of COVID-19 by using machine learning methods. Our dataset contains data from patients who are prone to the epidemic. It contains three types of lungs CT images (Normal, Pneumonia, and COVID-19) collected from two different sources; the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur, Pakistan, and the second one is a publicly free available medical imaging database known as Radiopaedia. For the preprocessing, a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest (ROIs) and acquire 52 hybrid statistical features for each ROIs. Also, 12 optimized statistical features are selected via the chi-square feature reduction technique. For the classification, five machine learning classifiers named as deep learning J4, multilayer perceptron, support vector machine, random forest, and naive Bayes are deployed to optimize the hybrid statistical features dataset. It is observed that the deep learning J4 has promising results (sensitivity and specificity: 0.987; accuracy: 98.67%) among all the deployed classifiers. As a complementary study, a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.     

An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods.     

Egocentric visual scene description based on human-object interaction and deep spatial relations among objects

Multimedia Tools and Applications - Visual Scene interpretation is one of the major areas of research in the recent past. Recognition of human object interaction is a fundamental step...     

Input Selection of Wavelet-Coupled Neural Network Models for Rainfall-Runoff Modelling

Water Resources Management - The use of wavelet-coupled data-driven models is increasing in the field of hydrological modelling. However, wavelet-coupled artificial neural network (ANN) models...     

Similarity measures of bipolar neutrosophic sets and their application to multiple criteria decision making

In this paper, we introduced some similarity measures for bipolar neutrosophic sets such as; Dice similarity measure, weighted Dice similarity measure, Hybrid vector similarity measure and weighted Hybrid vector similarity measure. Also we examine the propositions of the similarity measures. Furthermore, a multi-criteria decision-making method for bipolar neutrosophic set is developed based on these given similarity measures. Then, a practical example is shown to verify the feasibility of the new method. Finally, we compare the proposed method with the existing methods in order to demonstrate the practicality and effectiveness of the developed method in this paper.

     

A survey of decision making methods based on two classes of hybrid soft set models

To the best of our knowledge, the tool of soft set theory is a new efficacious technique to dispose uncertainties and it focuses on the parameterization, while fuzzy set theory emphasizes the truth degree and rough set theory as another tool to handle uncertainties, it places emphasis on granular. However, the real-world problems that under considerations are usual very complicated. Consequently, it is very difficult to solve them by a single mathematical tool. It is worth noting that decision making (briefly, DM) in an imprecise environment has been showing more and more role in real-world applications. Researches on the idiographic applications of the above three uncertain theories as well as their hybrid models in DM have attracted many researchers’ widespread interest. DM methods are not yet proposed based on fusions of the above three uncertain theories. In view of the reason, by compromising the above three uncertain theories, we elaborate some reviews to DM methods based on two classes of hybrid soft models: SRF-sets and SFR-sets. We test all algorithms for DM and computation time on data sets produced by soft sets and FS-sets. The numerical experimentation programs are written for given pseudo codes in MATLAB. At the same time, the comparisons of all algorithms are given. Finally, we expatiate on an overview of techniques based on the involved hybrid soft set models.     

A novel protocol for efficient authentication in cloud-based IoT devices

The Internet of Things (IoT) has emerged as one of the most revolutionary technological innovations with the proliferation of applications within almost all fields of the human race. A cloud environment is the main component of IoT infrastructure to make IoT devices efficient, safe, reliable, usable, and autonomous. Reduction in infrastructure cost and demand accessibility of shared resources are essential parts of cloud-based IoT (CIoT) infrastructure. Information leakage in cloud-assisted IoT devices may invite dangerous activities and phenomena. Various cloud-based systems store IoT sensor data and later on access it accordingly. Some of them are public, and some of them are private. Private cloud services must be secured from external as well as internal adversaries. Hence, there must be a robust mechanism to prevent unauthorized access to devices. This paper proposes a novel and efficient protocol based on the Elliptic Curve property known as Elliptic Curve Discrete Logarithm Problem (ECDLP) with hash and XOR functions for the authentication in cloud-based IoT devices. In comparison to the existing protocols, the proposed protocol is resistant to attacks and other security vulnerabilities. The one-way hash function and XOR function effectively ensure a reduction in computation cost. AVISPA and BAN logic have been used for formal analysis of the proposed protocol. As per the performance analysis results, it is clear that the proposed protocol is efficiently suitable for cloud-assisted IoT devices.

     

TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data

Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution (TIE) algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.