Department of Electrical Engineering

The smart grid has become an essential infrastructure for the modern power grid as new technology and communication networks are integrated, revolutionizing the energy sector. However, the enhanced interconnectivity and complexity of the smart grid have prompted fear regarding its susceptibility to cyber-attacks. This research paper provides a thorough examination of the difficulties encountered by cyber security in the smart grid and investigates inventive strategies to improve these threats. The research paper explores the complex and varied nature of cyber risks in the smart grid, including advanced attacks on essential infrastructure and possible weaknesses in communication protocols. The implementation of digital technologies, such as Smart Meter (SM), Advanced Metering Infrastructure (AMI), and Internet of Things (IoT), aims to enhance the efficiency, dependability, and sustainability of the system. Lastly, it explores potential advancements and upcoming technologies that show potential to improve the security of the smart grid, including blockchain, artificial intelligence, and quantum-resistant cryptography. A comprehensive analysis of the simulation findings for Load Frequency Control (LFC) has been thoroughly examined.

To maintain Power Integrity (PI) in a high-speed electronic system, the Power Delivery Network (PDN) needs to be optimized with respect to its impedance. To minimize power supply noise, decoupling capacitors are used in a PDN. By selecting appropriate decoupling capacitors (decaps) and placing them on optimal locations on board/package, the overall impedance of a power delivery network can be effectively reduced to a desired level to minimize the variationa in supply voltage due to varying load current. In this paper, a time-efficient matrix inversion approach is used for impedance calculation within Particle Swarm Algorithm (PSO) algorithm which helps to enhance the efficiency of the optimization proces. The proposed approach is demonstrated through a practical case study. The applied technique significantly reduces the overall runtime of the algorithm, enhancing computational efficiency. A comparative evaluation of the performance of the proposed approach with the conventional algorithm is presented.

In this paper, we investigate the pulse jamming effect in Power Line Communication (PLC) systems employing differential chaos shift keying (DCSK) over Log-normally distributed channel gain. Concurrently, the behavior of PLC channel noise is characterized by the Bernoulli-Gaussian random process. Further, the state of the jammer is modeled using a Bernoulli random variable. Depending upon the state of the jammer, we derive the probability density function (PDF) of the instantaneous signal-to-jamming-plus-noise ratio (SJNR) and the instantaneous signal-to-noise ratio (SNR). Furthermore, the series-based expressions of average bit error rate (ABER) and outage probability (OP) are analytically evaluated. Additionally, the asymptotic behavior of ABER is analyzed in terms of the coding gain and diversity order in the high SNR regime. Based on the ABER and OP analysis of the considered PLC system, some fruitful insights are demonstrated by observing the impact of different system parameters.

We propose a method to achieve stable oscillation of an additional mode, along with the highest gain mode, for achieving direct dual mode emission from a homogeneously broadened buried heterostructure laser.

Regular and continuous monitoring of uterine con-tractions aid in the diagnosis of critical pregnancies. Maternal and fetal complications can be timely intervened by obstetri-cians with uterine activity monitoring at homes. The aim of this paper is to reduce the number of channels for Uterine Magnetomyography (MMG) recording in order to facilitate its use in home uterine activity monitoring (HUAM). In this work, automatic channel minimization is presented by integrating Point Biserial Correlation with a comparative Merit based strategy and Local Channel Inclusion. An additional scheme of Channel Repeatability has been introduced in order to further reduce the channel set. As a result, the minimized array of 14 achieves a superior performance in comparison to the existing methods. Using Support Vector Machine (SVM) a classification accuracy, specificity and sensitivity of 78.33%, 85% and 75%, respectively, has been achieved.

This work conceives a novel sparse Bayesian learning (SBL)-based extended target parameter estimation scheme for an orthogonal frequency division multiplexing (OFDM) wave-form based-mmWave MIMO joint radar and communication (JRC) system. The proposed framework also incorporates the intercarrier interference (ICI) effect arising due to the Doppler shift together with radar clutter. The proposed algorithms are based on the hybrid mmWave MIMO architecture that requires a significantly fewer number of radio frequency (RF) chains in comparison to the number of antennas. A range, Doppler and angular (RDA)-domain representation of the target-plus-clutter echo is conceived toward target parameter estimation. The SBL framework is developed that exploits the 3-dimensional (3D)-sparsity arising in the RDA domain, given the limited number of targets and clutter, to jointly estimate the angles, range, velocity and radar cross-section (RCS) coefficients of an extended target. Simulation results demonstrate the imaging and accuracy of estimation of the target parameters in comparison to other existing techniques.

Owing to the exponential rise in the demand for high data rate communications, massive connectivity requirements in Internet of Things (IoT) based communication applications, and the spectral congestion issues in the traditional radio frequency (RF) based communications, visible light communications (VLC) have garnered attention of academia and industry due to their wide bandwidth, license free spectrum, energy efficiency, and low implementation cost. However, VLC systems are susceptible to interception due to their broadcast nature. In this paper, we investigate the effect of random pulse jamming attacks in indoor VLC systems assuming on-off keying (OOK). The user is assumed to be randomly located, following a uniform distribution, within a circular plane of maximum radius covered by the light emitting diode (LED) transmitter. We derive the cumulative distribution function (CDF) and probability density function (PDF) of the instantaneous signal-to-jamming-plus-noise ratio (SJNR) and the instantaneous signal-to-noise ratio (SNR), depending upon the state of the jammer. Utilizing the statistical characterization of the SJNR and SNR, we derive closed-form expressions of the average bit error rate (ABER) and lower bound on ergodic capacity (EC) of the considered VLC system. Useful insights into the considered VLC system performance are obtained through the impact of various jammer parameters on the system performance metrics.

In this paper, adaptive channel state information (CSI) estimation techniques are conceived for intelligent reflective surface (IRS)-assisted single input and single output (SISO) and multiple input multiple output (MIMO) systems. Initially, the input-output system model is derived for an IRS-assisted SISO system, and the block least mean square (BLMS) and block recursive least square (BRLS) techniques are proposed for adaptive CSI estimation. Subsequently, the system model is also determined for IRS-assisted MIMO systems, and the adaptive CSI estimation schemes described above are also extended to this scenario. Convergence analysis is presented and the asymptotic mean square error (MSE) of estimation expressions are determined for the BLMS and BRLS algorithms. Finally, the simulation results are presented to demonstrate the performance and also validate the analytical results derived for the above adaptive CSI estimation schemes for IRS-assisted SISO and MIMO systems.

In this paper, a broadband and high gain substrate integrated waveguide (SIW) cavity-backed slot antenna has been developed with a stacked configuration. The proposed antenna comprises a rectangular cavity with a bow-tie slot, an SIW, a microstrip line-to-SIW transition, and a stacked parasitic patch. An additional resonance is created at a higher frequency by properly optimizing the parasitic patch above the cavity-backed slot, improving the bandwidth and gain significantly. Compared with the SIW cavity-backed slot antenna, the impedance bandwidth and peak gain are increased from 5.12% (for |S11|≤slant - 10 dB) to 19.7% and 7.4 to 8.67 dBi, respectively. The overall size of the proposed antenna is 14 mm × 12 mm × 0.85 mm. The proposed design technique is easy to implement and promises to overcome the limitation of the narrow bandwidth of cavity-backed slot antenna without increasing its overall footprint.

This paper presents an innovative scheme for estimating the channel impulse response (CIR) in sparse multipath conditions for asymmetrically clipped direct current-biased op-tical OFDM (ADO-OFDM) visible light communication (VLC) systems, utilizing Bayesian learning (BL) techniques. We derive a multipath CIR model capturing both specular and diffusive reflections within the VLC system. Subsequently, we present a novel scheme for estimating the CIR in sparse multipath scenar-ios using the BL paradigm, which leverages the inherent sparsity of the multipath CIR in the delay domain. This scheme neces-sitates a constrained set of pilot subcarriers, thereby reducing pilot overhead when juxtaposed with traditional state-of-the-art channel estimation (CE) techniques. To assess the performance of the proposed BL-based paradigm for estimation, we compute the Oracle-MMSE (O-MMSE) along with the Bayesian Cramer Rao lower bound (BCRLB). Our extensive simulations reveal that even with a lower pilot overhead, the suggested BL method surpasses other conventional and sparse CE techniques across key metrics such as bit error-rate (BER) and normalized mean-square-error (NMSE).