A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything

Internet of Everything (IoE) indicates a fantastic vision of the future, where everything is connected to the internet, providing intelligent services and facilitating decision making. IoE is the collection of static and moving objects able to coordinate and communicate with each other. The moving objects may consist of ground segments and flying segments. The speed of flying segment e.g., Unmanned Ariel Vehicles (UAVs) may high as compared to ground segment objects. The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE (Ue-IoE). The routing maintenance overhead may increase when scaling the Ue-IoE (number of objects increases). A single change in topology can force all the objects of the Ue-IoE to update their routing tables. Similarly, the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease. The objects consume more energy on routing computations. To prevent the frequent updation of routing tables associated with each object, the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE. In this article, we propose a routing scheme in which the responsibility of route computation (from neighbor objects to destination) is assigned to some IoE-objects in the Ue-IoE. The route computation objects (RCO) are selected on the basis of certain parameters like remaining energy and mobility. The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects. The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption, messages overhead, throughput, delay etc.     

Liver-Tumor Detection Using CNN ResUNet

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest (ROI). This study uses ResUNet, an updated version of U-Net and ResNet Models that utilize the service of Residential blocks. We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients. The results showed the True Value Accuracy around 99% and F1 score performance around 95%. This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.     

Investigation of factors affecting dynamic modulus and phase angle of various asphalt concrete mixtures

This study investigated the dynamic response of various asphalt concrete (AC) mixtures subjected to sinusoidal loading. Eight AC mixtures (four wearing and four base course) were selected including (but not limited to): superpave, asphalt institute manual series, and dense bituminous macadam. The uniaxial dynamic modulus (|E*|) test at various temperatures (4.4–54.4 °C) and frequencies (0.1–25 Hz) was conducted using asphalt mixture performance tester. Statistical analysis of two-level factorial was employed to regulate the factors affecting the AC mixtures. The results revealed that an increase in temperature (from 21.1 to 37.8 °C), translated into 45 and 43 % drop in |E*| values on average while 80 and 67 % decrease in |E*| values was attributed to the sweep of frequency (from 25 to 0.1 Hz) for wearing and base course mixes, respectively. Non-linear regression model was developed to express the dynamic modulus as a function of test temperature, loading frequency and mixture volumetric parameter. Furthermore, Witczak model of dynamic modulus prediction was evaluated and the results indicated a close fit with an average under prediction error of 0.20. The study characterized and ranked the representative AC mixtures that could help in selecting the material/gradation for mechanistic-empirical pavement design approach.     

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.     

Framework for Effective Utilization of Distributed Scrum in Software Projects

There is an emerging interest in using agile methodologies in Global Software Development (GSD) to get the mutual benefits of both methods. Scrum is currently admired by many development teams as an agile most known methodology and considered adequate for collocated teams. At the same time, stakeholders in GSD are dispersed by geographical, temporal, and socio-cultural distances. Due to the controversial nature of Scrum and GSD, many significant challenges arise that might restrict the use of Scrum in GSD. We conducted a Systematic Literature Review (SLR) by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the challenges. To validate our review findings, we conducted an industrial survey of 305 practitioners. The results of our study are consolidated into a research framework. The framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated by five experts of distributed Scrum. Results of the expert review were found supportive, reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.     

An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution.     

Dynamic Stability Improvement of Decentralized Wind Farms by Effective Distribution Static Compensator

Dynamic instability of decentralized wind energy farms is a major issue to deliver continuous green energy to electricity consumers. This instability is caused by variations of voltage and frequency parameters due to intermittencies in wind power. Previously, droop control and inverter-based schemes have been proposed to regulate the voltage by balancing reactive power, while inertial control, digital mapping technique of proportional-integral-differential (PID) controller and efficiency control strategy have been developed to regulate the frequency. In this paper, voltage stability is improved by a new joint strategy of distribution static compensator (DSTATCOM) six-pulse controller based reactive power management among decentralized wind turbines and controlled charging of capacitor bank. The frequency stability is ensured by a joint coordinated utilization of capacitor bank and distributed wind power turbines dispatching through a new DSTATCOM six-pulse controller scheme. In both strategies, power grid is contributed as a backup source with less priority. These new joint strategies for voltage and frequency stabilities will enhance the stable active power delivery to end users. A system test case is developed to verify the proposed joint strategies. The test results of the proposed new schemes are proved to be effective in terms of stability improvement of voltage, frequency and active power generation.     

Electrostatic characterization and threshold voltage modeling of inversion type InGaAs gate-all-around MOSFET

Journal of Computational Electronics - This paper presents an analytical investigation of the electrostatic properties of a moderately doped symmetric gate-all-around nanowire MOSFET having InGaAs...     

Towards accurate measurement of the frequency dependence of capacitance and resistance standards up to 10 MHz

Progress toward an understanding of the frequency dependence of capacitance and resistance standards at frequencies up to 10 MHz is presented. A qualitative comparison is also made for capacitance and dissipation factor measurements between the National Physical Laboratory (NPL) high-frequency four terminal-pair (4TP) bridge and a commercial impedance analyzer for the first time. A set of novel high-frequency calculable coaxial resistance standards, of nominal 100 /spl Omega/ and 1 k/spl Omega/ values, have been developed and their calculated frequency dependence up to 1 MHz is given.     

Towards A Game Theoretic Understanding of Ad-Hoc Routing

In this paper we present a new application of game theory, in which game theoretic techniques are used to provide a rigorous underpinning to the analysis of ad-hoc routing protocols. The explosion of interest in ad-hoc networks over the last few years has resulted in a very large number of routing protocols being proposed. Despite this, the science of analysing routing protocols is still relatively immature, and the question that remains is how to decide “how good” a given protocol is. We propose a game theoretic approach as a potentially effective means of answering this question. The conceptual mapping of routing into a game is, we believe, natural and simple. Furthermore, game theory provides an extensive repertoire of tools to analyse key properties. The paper describes how routing techniques can be modelled as games and presents some analytical results.