A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network

Mobile location estimation has received considerable interest over the past few years due to its great potential in different applications such as logistics, patrol, and fleet management. Many mobile location estimation techniques had been proposed to improve the accuracy of location estimation. Location estimation based on artificial intelligence techniques is a recent alternative approach. In this paper, adaptive neuro-fuzzy inference system (ANFIS) is used as a robust location estimator to locate the mobile station (MS) using the MS geo-fencing area data within 9 km from a serving base station. Extensive evaluations and comparisons have been performed, and a set of statistical parameters has been obtained. From the comparison of the proposed ANFIS estimator with the neural-network-based estimators, it is found that ANFIS estimator is faster and more robust. Its average computation time (ACT) is 0.076 sec. While the ACT for multilayer perceptron (MLP) and radial-based function (RBF) neural networks is 0.88 and 1.7, respectively. Whereas on comparing ANFIS with other techniques, it is found that in ANFIS estimator, 67 percent of the estimated location errors do not exceed 149 m, while these for the statistical, multiple linear regression, and geometric are 170, 280, and 2,346 m, respectively. Thus, the results clearly reveal that the proposed ANFIS estimator outperforms all other techniques.     

Flow-based control of temperature in long ducts

Flow-based control of a thermal system with a long duct and heat loss to the environment is analyzed. A Proportional-Integral controller is used to regulate the duct outlet temperature by using the flow velocity as control input. The one-dimensional energy equation in Eulerian and Lagrangian forms are numerically solved. The non-linear dynamics can be represented by an integral equation in terms of the residence time which acts as a delay. A linear stability analysis leads to a characteristic transcendental equation which is examined for different orders of the residence time. Pontryagin’s theorem on the zeros of exponential polynomials is used to obtain stability maps as a function of system parameters. Numerical simulations are performed to verify the predictions, determine super- and sub-critical instabilities, and evaluate the amplitude and frequency of limit-cycle oscillations.     

Computational study of band-crossing reactions

A numerical study of band-crossing reactions is conducted using a quasi-one-dimensional (1-D) computational model that accounts for species bulk advection, electromigration velocities, diffusion, and chemical reaction. The model is used to simulate chemical reactions between two initially distinct sample zones, referred to as "bands," that cross each other due to differences in electromigration velocities. The reaction is described in terms of a single step, reversible mechanism involving two reactants and one product. A parametric study is first conducted of the behavior of the species profiles, and results are interpreted in terms of the Damko/spl uml/hler number and of the ratios of the electromigration velocities of the reactant and product. Computed results are then used to explore the possibility of extracting forward and backward reaction rates based on time resolved observation of integral moments of species concentrations. In particular, it is shown that in the case of fast reactions, robust estimates can be obtained for high forward rates, but that small reverse rates may not be accurately observed.     

Food neophobia levels of Lebanese and American college students

Food neophobia, defined as the reluctance to eat novel foods, is a personality trait that influences everyday human food choices. The objectives of this work were first, to compare food neophobia levels among American and Lebanese college students (n = 1122), second to assess the effect of personal variables such as country of residence, socio-economic status (SES) on food neophobia levels, and third to examine the effect of food neophobia levels on the familiarity and willingness to try ratings of familiar and novel foods. Average food neophobia scale (FNS) score for all respondents was 33.1 ± 11.3. Differences on FNS scores were obtained between American (29.8) and Lebanese (36.4) students (P < 0.05). Number of trips taken outside the country, frequency of eating ethnic foods and history of sickness after eating a new food were significant (P < 0.05). Food neophilic subjects had higher familiarity and willingness to try scores for familiar and novel foods.     

A survey of power estimation techniques in VLSI circuits

With the advent of portable and high-density microelectronic devices, the power dissipation of very large scale integrated (VLSI) circuits is becoming a critical concern. Accurate and efficient power estimation during the design phase is required in order to meet the power specifications without a costly redesign process. In this paper, we present a review of the power estimation techniques that have recently been proposed     

A technique for Improving dual-output domino logic

We present a technique, termed clock-generating (CG) domino, for improving dual-output domino logic that reduces area, clock load and power without increasing the delay. A delayed clock, generated from certain dual-output gates, is used to convert other dual-output gates to single output. Simulation results with ISCAS 85 benchmark circuits indicate an average reduction in area, clock load, and power of 17%, 20%, and 24%, respectively, over dual-output domino and a 48% power reduction for the largest circuit.     

Effect of tactile feedback on movement speed and precision during work-related tasks using a computer mouse

Effects of tactile feedback on movement accuracy and speed were studied. Younger and older participants performed three tasks (1, select and drag word; 2, menu navigation; 3, select and drag cell) using commercial software and a mouse with or without tactile feedback. Task time and error number were recorded. Tasks were divided according to presence or absence of tactile feedback, and participants were divided into subgroups (high, average, low) based on Task 1 performance. Overall, older participants took longer (p < .0001) and made more errors (p < .001) than younger participants. There was an effect of feedback by task in younger participants for all six outcomes (p < 0.02). At the task level, with feedback, younger participants reduced performance time (13%) and errors (24%) on Task 1. Low- and average-performance younger participants benefited most from feedback for Task 1. Older low-performance participants also benefited from feedback for Task 1. For Task 3, older participants tended to take more time and make more errors with feedback. Tactile feedback may enhance performance when feedback is event related. Older people may not integrate sensation as well as younger individuals to enhance performance. Potential applications of this research include the development of tactile feedback interfaces to facilitate computer use.     

Crowdsourced Requirements Engineering Challenges and Solutions: A Software Industry Perspective

Software crowdsourcing (SW CS) is an evolving software development paradigm, in which crowds of people are asked to solve various problems through an open call (with the encouragement of prizes for the top solutions). Because of its dynamic nature, SW CS has been progressively accepted and adopted in the software industry. However, issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained. If the requirements are not clear to the development team, it has a significant effect on the quality of the software product. This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering (RE) process. Moreover, solutions to overcome these challenges are also identified. Qualitative data analysis is performed on the interview data collected from software industry professionals. Consequently, 20 SW–CS based RE challenges and their subsequent proposed solutions are devised, which are further grouped under seven categories. This study is beneficial for academicians, researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.     

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL-CTD model with increased accuracy of 99.47%.