Amit K. Shukla

Real-time power aware scheduling for tasks with type-2 fuzzy timing constraints

The timing constraint of tasks in the mobile real-time computing systems plays the central role in deciding the task schedule as timely completion of the task is very important in such systems. These timing constraints are however completely unquantifiable during the time of system modeling and designing. Thus we consider type-2 fuzzy sets for modeling the timing constraints in mobile and time-critical computing systems and propose a new algorithm FT2EDF (Fuzzy Type-2 Earliest Deadline First) for task scheduling. On the other hand, because of the limitation of the storage power, power efficiency is another foremost design objective for designing mobile real-time computing systems. However, reduction of processor power pulls down the system performance. Timely task completion and power efficiency are therefore two mutually conflicting criteria. In this paper, we propose a heuristic based solution approach that with a modified version of the non-dominated sorting genetic algorithm-II (NSGA-II). Our approach allows that a processor dynamically switches between different voltage levels to ensure optimum reduction in the power requirements without compromising the timeliness of the task completion. The efficacy of our approach is demonstrated with two numerical examples. Comparison with the previous results show that our solution ensures approximately 44% of energy saving as compared to the around 25% of the earlier results.

Semi-elliptic membership function

A potentially important, yet under-stated membership function (MF) shape viz. semi-elliptic membership function (SEMF) has been elaborated in this paper so that researchers are able to see its true potential for realistic modelling of the decision variables in real-life applications. The concept of semi-elliptic membership function has the practical significance since it can nicely remove the drawbacks associated with the Gaussian membership function shape regarding its long tail with non-zero membership values. A semi-elliptic membership function can be mathematically expressed in terms of only two parameters. It is shown that, with the representation considered here, prominent arithmetic operations such as addition, subtraction, multiplication etc. can be applied on the semi-elliptic fuzzy numbers. As an extension, a novel membership function generation technique for interval type-2 semi-elliptic (IT2SE) MF has also been proposed. Then the applicability of the arithmetic operations on the IT2SE fuzzy numbers is shown. It is illustrated that semi-elliptic fuzzy numbers can suitably be ranked by existing distance measures. We have shown that the well-known INT (Improved Nie-Tans) defuzzification technique can be applied to the interval type-2 semi-elliptic fuzzy sets for producing defuzzified outputs. Finally, with application point of view, semi-elliptic fuzzy numbers has been applied to the real-time task scheduling problem. Results are compared with triangular fuzzy numbers. Suitable numerical examples are considered for demonstration purposes.

Energy efficient task scheduling with Type-2 fuzzy uncertainty

In this paper, we have reported a new approach for employing the non dominated sorting genetic algorithm-II (NSGA-II) with the type-2 fuzzy sets in optimizing energy in real-time embedded systems. The multi-objective problem of energy efficiency and timeliness of tasks has been extensively studied. Little variations in the task timing parameters produce considerable variations in the results of the critical real-time computations. Importantly, at the system designing phase these timing parameters are completely unquantifiable. We therefore propose here a new algorithm for real-time scheduling in type-2 fuzzy uncertain domains. We have included comparative results obtained from models with crisp timing parameters and their fuzzy type-1 and type-2 counterparts. From the observations of the outcome, it is found that model with crisp timing parameters gives the worst result as energy consumption in the system is maximum at a constant earliness. The crisp model is outperformed by both fuzzy type-1 and type-2 models and ensures significant reductions in energy consumption. Whereas fuzzy type-2 model overwhelms both fuzzy type-1 and crisp model in ensuring task completions with maximum earliness. Suitable numerical examples are included to demonstrate our proposed approach.

Atanassov Intuitionistic Fuzzy Domain Adaptation to contain negative transfer learning

Transfer learning framework is designed to use previously acquired knowledge to solve a new but somewhat related task (like humans do). Non-availability of sufficient and relevant information in building a learning model is a major bottleneck in this research area. However, such models are highly susceptible to negative transfer learning (NTL) during transferral of knowledge due to the hesitancy in the decision making. Negative transfer learning may cause chaotic learning and have a profound effect on their predictive precision. In this paper, we have proposed a novel Intuitionistic Fuzzy Domain Adaptation (IFDA) algorithm, which uses Yager-generating function over Atanassovs Intuitionistic fuzzy set theory in conjunction with modified Hausdorff Intuitionistic similarity metric to build a fuzzy domain adaptation algorithm which is independent of supervised machine learning technique. It exploits the hesitancy margin in intuitionistically fuzzified features by eradicating similar looking but useless instances. Therefore, it selects optimal source instances from a previous problem in bridging the knowledge gap, in order to solve a new target problem, by containing negative transfer learning.

A Review of the Scopes and Challenges of the Modern Real-Time Operating Systems

A real-time operating system RTOS is an integral part of a real-time embedded system RTES. Most of the RTESs work on dynamic environments, and hence, the computational cost of tasks cannot be calculated in advance. Thus, RTOSs play a significant role in the smooth operations of the RTES through efficient task scheduling schemes and resource managements. This article investigates the existing design challenges and scope of the modern RTOSs. A wide variety of latest RTOSs are discussed and elaborated in detail. A comparative study with their prospects has been explained so that interested readers can use the article as a readily available starting point for their further studies on this topic.

Applied soft computing: A bibliometric analysis of the publications and citations during (2004–2016)

Abstract The Journal of Applied Soft Computing (ASOC) is a highly reputed journal in the field of engineering and computer science. This study reviews the ASOC publications during the period 2004–2016 which are indexed in the Web of Science (WoS). The motive behind this study is to reveal the main influencing aspects that govern the ASOC publications and its citation structure using scientometric methods The citation structure of the journal is analyzed first, which includes the distribution of citations over the years, citing sources and an aerial view of the citation structure. Then, the ASOC authorship is analyzed and the author co-citation network is displayed. Further, a country-wise temporal and quantitative analysis of the publications is given along with the highly cited documents among the ASOC publications. Document co-citation analysis is also performed to reveal the intellectual base of the ASOC publications.

A Scientometric Study of Neurocomputing Publications (1992–2018): An Aerial Overview of Intrinsic Structure

The international journal of neurocomputing (NC) is considered to be one of the most sought out journals in the computer science research fraternity. In this paper, an extensive bibliometric overview of this journal is performed. The bibliometric data is extracted from the Web of Science (WoS) repository. The main objective of this study is to reveal internal structures and hidden inferences, such as highly productive and influential authors, most contributing countries, top institutions, collaborating authors, and so on. The CiteSpace and VOS viewer is used to visualize the graphical mapping of the bibliometric data. Further, the document co-citations network, cluster detection and references with strong citation burst is analyzed to reveal the intellectual base of NC publications.

Interval type-2 fuzzy sets for enhanced learning in deep belief networks

Restricted Boltzmann Machine (RBM) is a generative, stochastic neural network with two separate layers of hidden and visible units. Training data samples in RBM are usually corrupted by noise. RBM is not robust enough to handle such noises, which leads to uncertainty. In the literature, Fuzzy RBM (FRBM) has already been proposed for enhancing deep learning. In FRBM, the parameters of RBM are modelled as type-1 fuzzy numbers. However, there can be multiple sources of uncertainties such as noises in the data measurements, variations in the environment where they are deployed, etc. Such uncertainties cannot be modelled by type-1 fuzzy sets (T1 FS), since their membership values are themselves crisp in nature. On the other side, IT2 FS can model higher order uncertainties with their fuzzy membership grades. So, we propose to use interval type-2 fuzzy sets (IT2 FS) to model uncertain parameters of RBMs in the learning stage. Since deep neural network (DNN) is pre-trained using stacked RBMs, modeling noises using IT2 FS would demonstrate high performance and low root mean square error (RMSE) while learning. Thus, we propose a new algorithm viz. Interval type-2 fuzzy set based approach for enhanced deep learning (IT2 FS-EDL) in which RBM parameters are modeled as type-2 fuzzy sets in the learning process. Numerical examples and experimentations have been demonstrated to present the suitability of our proposed approach.