Department of Electrical Engineering

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IIT Jodhpur

Prevention of falls requires providing a small number of recommendations based on the risk factors present for a person. This article deals with the evaluation of 12 modifiable risk factors for fall, based on a selection of 45 variables from a real data set. The results of four classifiers (Logistic Regression, Random Forest, Artificial Neural Networks, and Bayesian Networks) are compared when using the initial imbalanced data set, and after using the balancing method SMOTE. We have compared the results using four different measures to evaluate their performance (balanced accuracy, area under the Receiver Operating Characteristic (ROC) curve F1-score, and F2-score). The results show that there is a significant improvement for all the classifiers when classifying each target risk factor using the data after balancing with SMOTE. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

World University root node

International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings

Evaluation of Risk Factors for Fall in Elderly People from Imbalanced Data using the Oversampling Technique SMOTE

The massive growth in mobile users and wireless technologies has resulted in increased data traffic and created demand for additional radio spectrum. This growing demand for radio spectrum has resulted in spectrum congestion and mandated the need for coexistence between radar and interfering communication emitters. To address the aforementioned issues, it is critical to review existing policies and evaluate new technologies that can utilize spectrum in an efficient and intelligent manner. Cognitive radio and cognitive radar are two promising technologies that exploit spectrum using dynamic spectrum access techniques. Additionally, introducing the bio-inspired concept ‘metacognition’ in a cognitive process has shown to increase the effectiveness and robustness of the cognitive radio and cognitive radar system. Metacognition is a high-order thinking agent that monitors and regulates the cognition process through a feedback and control process called the perception–action cycle. Extensive research has been done in the field of spectrum sensing in cognitive radio and spectral coexistence between radar and communication systems. This paper provides a detailed classification of spectrum sensing schemes and explains how dynamic spectrum access strategies share the spectrum between radar and communication systems. In addition to this, the fundamentals of cognitive radio, its architecture, spectrum management framework, and metacognition concept in radar are discussed. Furthermore, this paper presents various research issues, challenges, and future research directions associated with spectrum sensing in cognitive radar and dynamic spectrum access strategies in cognitive radar. © 2022 Elsevier B.V.

Physical Communication

Spectrum sensing in cognitive radio networks and metacognition for dynamic spectrum sharing between radar and communication system: A review

This paper considers the problem of placing all the poles arbitrarily for a linear time-invariant plant with the linear part 00 sliding mode control. We solve this problem in two ways. In the first approach, we design a sliding mode control by specifying the desired pole locations. The closed-loop system under this control law has all eigenvalues at the desired places. In the second approach, the sliding mode control is designed from a given state feedback gain so that all the poles of the closed-loop system are placed at the same location as that of the state feedback controller. Here, we provide a necessary and sufficient condition for the existence of a linear gain using the sliding mode control to achieve the desired pole assignment. This condition is always fulfilled for the single input case whereas it is only applicable for certain multi-input scenarios that meet the conditions stated in the paper. In both the approaches, one can place the closed-loop poles with the proposed sliding mode control at any arbitrary location in the left half of the complex plane, unlike with traditional design, where m poles are at the origin with m being the number of control inputs. A numerical example illustrates the proposed design methodology for sliding mode control. © 2022 IEEE.

Proceedings of IEEE International Workshop on Variable Structure Systems

Arbitrary Pole Placement with Sliding Mode Control

Department of Mechanical Engineering

Nanotechnology

1T and 2H Heterophase MoS2 for Enhanced Sensitivity of GaN transistor-based Mercury ions Sensor

Stability and stabilization with predefined upper bounded settling time (PUBST) for the nonlinear nonautonomous systems are studied in this paper. The concepts of input to state stability are extended to the control problems exhibiting the property of convergence with PUBST (PUBSTC). The Lyapunov functions for depicting the presence of PUBST are introduced and reffered to as PUBSTC Lyapunov functions. Furthermore, a robust sliding mode control allowing the convergence with PUBST is designed for the nonlinear systems with matched disturbances and referred to as PUBSTC-SMC. These developments are accompanied by several academic examples for better understanding. © 2022 IEEE.

Stabilization of Continuous Time Uncertain Systems with Predefined Upper Bound of Settling Time

In this paper, implicit systems with uncertain input matrix subjected to matched Lipschitz disturbances are considered. For such kind of systems a Lipschitz continuous control, achieving admissible solutions for implicit systems is proposed. The developed methodology consists of two steps. In the first step a stable sliding surface is designed after transforming the original system into a regular like form. Then a first order sliding mode controller through an integrator is designed which will ensure finite-time convergence to the sliding manifold and the exponential convergence to the origin in spite of the presence of Lipschitz disturbances and uncertain input matrix. © 2022 European Control Association

European Journal of Control

A Lipschitz continuous sliding mode control for implicit systems

Over the past few years, rated convergence has gained importance and has acquired tremendous interest from the research community. In this regard, some nomenclatures have emerged, broadly falling under the spectrum of finite time stability. However, these notions have certain aspects which differentiate them. Therefore it becomes imperative to bring out a comparative study among these notions. Moreover, such recent concepts are being applied to several application problems. Thus it is essential to understand even the small details associated with them. The purpose of the present paper is to address these objectives. In addition, the convergence of states and boundedness of the control in case of arbitrary time convergence has also been discussed. © 2022 IEEE.

IECON Proceedings (Industrial Electronics Conference)

On the Predefined, Prescribed and Arbitrary Time Convergence

In this technical note, free-will arbitrary time terminal sliding mode control design is proposed. With such a design, it is possible to control the time duration of sliding in the sliding phase. Under the assumption that the chosen arbitrary time is greater than the reaching phase time (tr), it is shown that the nth order chain of integrators converges to zero within the selected arbitrary time. An algorithm is also proposed for the case when tr is not known to the designer. Stability analysis based on Lyapunov theory has been provided. © 2004-2012 IEEE.

IEEE Transactions on Circuits and Systems II: Express Briefs

Free-Will Arbitrary Time Terminal Sliding Mode Control

Quantifying image naturalness using differential curvelet features

<a href="https://www.sciencedirect.com/topics/computer-science/machine-learning">Machine learning</a> (ML) in general and deep learning (DL) in particular has become an extremely popular tool in several vision applications (like object detection, super resolution, segmentation, object tracking etc.). Almost in parallel, the issue of explainability in ML (i.e. the ability to explain/elaborate the way a trained ML model arrived at its decision) in vision has also received fairly significant attention from various quarters. However, we argue that the current philosophy behind explainable ML suffers from certain limitations, and the resulting explanations may not meaningfully uncover black box ML models. To elaborate our assertion, we first raise a few fundamental questions which have not been adequately discussed in the corresponding literature. We also provide perspectives on how explainablity in ML can benefit by relying on more rigorous principles in the related areas.