Indian Institute of Technology Delhi

Consider the Poincaré-Sobolev inequality on the hyperbolic space: for every $n \ge 3$ and $1 < p \le \frac{n+2}{n-2},$ there exists a best constant $S_{n,p, \lambda }({\mathbb {B}}^{n})>0$ such that $\begin{aligned} S_{n, p, \lambda }({\mathbb {B}}^{n})\left( ~\int \limits _{{\mathbb {B}}^{n}}|u|^{{p+1}} \, \textrm{d}v_{{\mathbb {B}}^n} \right) ^{\frac{2}{p+1}} \le \int \limits _{{\mathbb {B}}^{n}}\left( |\nabla _{{\mathbb {B}}^{n}}u|^{2}-\lambda u^{2}\right) \, \textrm{d}v_{{\mathbb {B}}^n}, \end{aligned}$holds for all $u\in C_c^{\infty }({\mathbb {B}}^n),$ and $\lambda \le \frac{(n-1)^2}{4},$ where $\frac{(n-1)^2}{4}$ is the bottom of the $L^2$-spectrum of $-\Delta _{{\mathbb {B}}^n}.$ It is known from the results of Mancini and Sandeep (Ann. Sc. Norm. Super. Pisa Cl. Sci. 7 (4): 635–671, 2008) that under appropriate assumptions on n, p and $\lambda$ there exists an optimizer, unique up to the hyperbolic isometries, attaining the best constant $S_{n,p,\lambda }({\mathbb {B}}^n).$ In this article, we investigate the quantitative gradient stability of the above inequality and the corresponding Euler-Lagrange equation locally around a bubble. Our result generalizes the sharp quantitative stability of Sobolev inequality in ${\mathbb {R}}^{n}$ by Bianchi and Egnell (J. Funct. Anal. 100 (1): 18–24. 1991) and Ciraolo, Figalli and Maggi (Int. Math. Res. Not. IMRN (21): 6780–6797, 2018) to the Poincaré-Sobolev inequality on the hyperbolic space. Furthermore, combining our stability results and implementing a novel and refined smoothing estimates in spirit of Bonforte and Figalli (Comm. Pure Appl. Math. 74 (4): 744–789, 2021), we prove a quantitative extinction rate towards its basin of attraction of the solutions of the sub-critical fast diffusion flow for radial initial data. In another application, we derive sharp quantitative stability of the Hardy-Sobolev-Maz’ya inequalities for the class of functions which are symmetric in the component of singularity.

A key advantage of combining the exceptional properties of graphene with conducting polymers, lies in their remarkable property tunability through filler additions into polymer matrices, with synthesis routes playing a crucial role in shaping their characteristics. In this work, we examine the electronic properties of polyaniline and graphene nanocomposites synthesized via a simple solution mixing method, which offers advantages such as ease of use and efficiency. Increasing graphene content enhances nanocomposite conductivity, and a percolation effect is observed. The percolation threshold is high and is consistent with a strong role played by voids in the structure. Temperature-dependent conductivity measurements highlight three distinct conduction regimes: insulating, critical, and metallic. These findings underscore the significant influence of synthesis method and structural disorder on shaping electronic properties, paving the way for engineering multifunctional nanocomposites with exceptional versatility and performance.

The dehydrogenation of ammonia borane in the presence of a variety of Lewis acids such as ICl, IBr, Br2, CuCl2, AlCl3, GaCl3, InCl3, and [Ph3C]BF4 at a concentration of 7.5 mol % was effective in selectively producing aminodiborane (μ‐NH₂B₂H₅, ADB) at 80 °C. Compounds such as ICl, IBr, Br2, AlCl3, and GaCl3 at the same concentration could also generate ADB at lower temperatures of 35 °C and 50 °C. In contrast, BX3 (X=Cl, Br) at the same concentration of 7.5 mol % was found to give exclusively B2H6. Further, selective synthesis of diborane or ADB was achieved by adjusting the stoichiometry of the boron trihalides. A concentration of 7.5 mol % (upto 1 equivalent) of BBr3 favored the formation of B2H6, while 1 mol % BBr3 predominantly yielded ADB. Interestingly, both ADB and B2H6 facilitated the reduction of acetanilides. A mechanism has been proposed for both diborane and ADB formation using these Lewis acids.

Carbenes in general and isolable NHCs (N-heterocyclic carbenes) in particular have been useful ligands in recent years. The emergence of CAACs [cyclic(alkyl)(amino)carbenes], BICAACs [bicyclic(alkyl)(amino)carbenes], and many other carbenes has marked...

The dual‐site insertion of GeCl2 into bis‐1,3‐N,S‐chelated ruthenium complexes, [(PPh3)2Ru{κ2‐N,S‐(L)2}], (cis‐1L1 and cis‐1L2; L = NC5H4S (L1); L = NC7H4S2 (L2)) has been investigated by using the electron‐rich behavior of these ruthenium systems. The treatment of cis‐1L1 and cis‐1L2 with GeCl2.dioxane led to the isolation of dual‐site GeCl2 inserted [(PPh3)2Ru{κ2‐Ge,S‐(GeCl2L)2}], (cis‐2L1 and cis‐2L2) and mono‐site GeCl2 inserted product [(PPh3)2{κ2‐Ge,S‐(GeCl2L)}Ru(κ2‐N,S‐L)], (trans‐3L1 and trans‐3L2). The cis‐2L1 and cis‐2L2 have two fused 5‐membered (RuGeNCS) rings, while the trans‐3L1 and trans‐3L2 have one 5‐membered (RuGeNCS) and one 4‐membered (RuNCS) ring. The molecular structure of cis‐2L1 shows that both the germanium centers have distorted tetrahedral geometry. Similarly, treatment of mono‐1,3‐N,S‐chelated ruthenium complex, [{κ2‐N,S‐(NS2C7H4)}Ru{κ3‐H,S,S‐H2B(NC7H4S2)2}PPh3], trans‐4 with GeCl2.dioxane yielded Ge‐inserted complex, [{κ2‐Ge,S‐(GeCl2NS2C7H4)}Ru{κ3‐H,S,S‐H2B (NC7H4S2)2}], (trans‐5). Further, to study the possibility of inserting GeCl2 into the Ge(II)‐S bond, we have made mercapto ligand functionalized germylenes, [{(iBu)2ATI}Ge (NC5H4S)] (6L1) and [{(iBu)2ATI}Ge(NC7H4S2)] (6L2).

This article presents a three-phase, single-stage photovoltaic (PV) system with a battery energy storage (BES) that functions in both grid-tied and islanded modes. In grid-tied mode, dual-mode PV-battery system (DPBS) provides functionalities such as reactive power compensation, enhanced power quality (PQ), and feeding PV power to utility. On grid loss, it acts as an uninterrupted supply and maintains load voltage. To provide above features, a dual-mode controller is designed with cascaded adaptive vectorial filter control along with maximum power point tracking (MPPT) in grid-tied mode and voltage control in islanded mode. Moreover, a robust dual-loop adaptive notch filter-based phase-locked loop (PLL) with a hybrid islanding detection mechanism monitors grid and coordinates grid re/connection and isolation, adhering to IEEE 1547-2018 revised standard. A laboratory prototype of DPBS has operated successfully in different test scenarios.

This paper presents an artificial neural network (ANN) approach for the development of position/speed sensorless switched reluctance motor (SRM) drive. A precise estimation of rotor position is essential for reliable operation of the drive. Due to its non-linear saturation characteristics, SRM poses a challenging task in modelling mathematically for developing control algorithm. In this work, a neural network uses a real-time magnetic data, which is mapped through supervised training by using Levenberg–Marquardt (LM) based back propagation (BP) learning algorithms in a Simulink environment. Neuron number play a dominant role while training the network and impacts fitting level. Therefore, network model is optimized for number of neurons in the internal layers. Besides position sensorless scheme, the system uses an advance angle control for controlling the speed of SRM drive, eliminating the switching losses incurred by conventional hysteresis controller. Response of the drive during steady state and transient conditions is validated experimentally, accounting its suitableness for the control of SRM.

Intelligent reflecting surface (IRS) has recently emerged as a promising technology for beyond fifth-generation (B5G) networks conceived from metamaterials that smartly tunes the signal reflections via a large number of low-cost passive reflecting elements. However, the IRS-assisted communication model and the optimization of available resources needs to be improved further for more efficient communications. This paper investigates the enhancement of received power in an IRS-assisted wireless communication by jointly optimizing the phase shifts at the IRS elements and its location. Employing the conventional Friss transmission model, the relationship between the transmitted power and reflected power is established. The expression of the received power incorporates the free space loss, reflection loss factor, physical dimension of the IRS panel, and radiation pattern of the transmit signal. Also, the expression of reflection coefficient of IRS panel is obtained by exploiting the existing data of radar communications. Initially exploring a single IRS element within a two-ray reflection model, we extend it to a more complex multi-ray reflection model with multiple IRS elements in 3D Cartesian space. The expression of the received power in both the cases is derived in a more tractable form, and then, it is maximized by jointly optimizing the underlying variables, i.e., the IRS location and the phase shifts. Further, the optimization of resources are investigated in active IRS, multiple access, and joint active and passive beamforming. Numerical insights and performance comparison reveal that joint optimization leads to a substantial 37% enhancement in received power compared to the closest competitive benchmark.

Herein, minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source setting is considered. The EH source opportunistically samples one or multiple processes over discrete time instants and sends the status updates to a sink node over a wireless fading channel. Each time, the EH node decides whether to probe the link quality and then decides whether to sample a process and communicate based on the channel probe outcome. The trade-off is between the freshness of information available at the sink node and the available energy at the source node. We use infinite horizon Markov decision process (MDP) to formulate the AoI minimization problem for two scenarios where energy arrival and channel fading processes are: (i) independent and identically distributed (i.i.d.), (ii) Markovian. In i.i.d. setting, after channel probing, the optimal source sampling policy is shown to be a threshold policy. Also, for unknown channel state and EH characteristics, a variant of the Q-learning algorithm is proposed for the two-stage action model, that seeks to learn the optimal policy. For Markovian system, the problem is again formulated as an MDP, and a learning algorithm is provided for unknown dynamics. Finally, numerical results demonstrate the policy structures and performance trade-offs.

Interleaved current fed dual active bridge (CFDAB) converter is a potential converter for low voltage battery charging applications. The output inductors, which are present on the low voltage side of CFDAB, provide ripple-free battery current and shoot through protection under fault conditions. The behavior of this converter slightly differs from the voltage fed dual active bridge (VFDAB) converter and is less studied in literature. This paper presents the study in two parts. Firstly, an improved Fourier domain model of CFDAB is presented which can seamlessly incorporate numerous harmonics for better large signal model accuracy unlike conventional generalized average modeling (GAM) technique. Furthermore, the small signal model is constructed for selective dominant harmonics in order to preserve simplicity. The presented Fourier domain model is based on Harmonic state space (HSS) model. Secondly, it discusses the presence of two resonances in the small signal model of CFDAB. One of them lies at switching frequency while other lies at converter natural frequency. The latter resonance either increases the steady state ripple or decreases the bandwidth of the conventional closed loop current controller. Hence, this paper also presents adaptive notch integrated PI controller in order to overcome above drawbacks of simple PI controller. Simulations are carried out in MATLAB/Simulink. Experimental investigations are carried out on a laboratory prototype. Simulation and experimental results confirm the plausibility of the model and controller.

In this study, we jointly investigate the connectivity probability and the number of required resource blocks (RBs) for load reduction during handoff (HO) decision for hybrid vehicle-to-everything (V2X) communication. Frequent HOs is a major issue in vehicular communication, causing various concerns such as loading problems in the overlapping area, signalling overhead, and decreasing the bandwidth efficiency of the network. To combat the loading problem due to HO in the overlapping area and the low convergence issues, we propose a game-theoretic approach to resolve the imbalance of resource allocation amongst the clusters at the edge of the coverage area. The proposed game-based Flexible Resource Allocation (FRA) approach optimally redistributes the resources to all clusters within a g-NodeB (g-NB). We obtain a closed-form expression of the transmit power for a cellular-vehicle-to-everything (C-V2X) standard of 5G network, besides analyzing the effects of connectivity probability and required resources for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) during HO. We explore the implications of non-uniform speeds among the clusters and perform an analysis of time complexity. Additionally, we compare two types of cost functions. A linear cost function links a player's cost to their action proportionally, while a nonlinear cost function shows a non-proportional relationship between a player's cost and their action. The simulation results indicate that, with an increasing number of clusters, the linear cost case outperforms the non-linear case in both connectivity probability (which increases by 30.18%) and the number of required resources (which is reduced by 39%) in the HO region. The analytical derivation has been verified through simulation results.

This paper deals with an advanced discrete generalized integrator frequency-locked loop (DGI-FLL) control algorithm designed for regulating grid-side converter (GSC) of doubly-fed induction generator (DFIG). Its primary function is to ensure consistent regulation of DC-bus voltage of rotor side converter (RSC) and GSC. Notably, this advanced DGI-FLL control demonstrates robust performance even amidst fluctuations in wind speed and varying loads. By implementing advanced DGI-FLL control, notable enhancements in system dynamics such as improved rise time, delay time, and settling time are achieved across diverse load conditions. Furthermore, this control algorithm effectively addresses power quality (PQ) concerns associated with DFIG. Synchronization of DFIG stator windings with local grid is a pivotal aspect, with local grid established through a combination of a battery and a double-stage photovoltaic (PV) supported grid forming converter (GFC). Initially, open-circuit voltages and frequency are generated across stator windings of DFIG via stage-I RSC control. Subsequently field-oriented control (FOC) algorithm takes charge of RSC post-stator synchronization. This algorithm not only fulfills reactive component requirement of DFIG but also maximizes power extraction from wind turbine. Moreover, an energy storage system (ESS) based on batteries is integrated to store surplus energy and subsequently distributes it based on load demand. This paper presents comprehensive test results showcasing integrated performance of hybrid microgrid at varying load conditions, solar insolation and changes in wind speed. Notably, it demonstrates how total harmonics distortion (THD) for both current and voltage at point of common coupling (PCC) adhere to the standard set forth by the IEEE 519-2022.Std.

Caches are crucial yet power-hungry components in present-day computing systems. With the Negative Capacitance Fin Field-Effect Transistor (NCFET) gaining significant attention due to its internal voltage amplification, allowing for better operation at lower voltages (stronger ON-current and reduced leakage current), the introduction of NCFET technology in caches can reduce power consumption without loss in performance. Apart from the benefits offered by the technology, we leverage the unique characteristics offered by NCFETs and propose a dynamic voltage scaling based criticality-aware performance and energy optimization policy (CAPE) for on-chip caches. We present the first work towards optimizing energy in NCFET-based caches with minimal impact on performance. Compared to operating at a nominal voltage of 0.7V, CAPE shows improvement in Last-Level Cache (LLC) energy savings by up to 19.2%, while the baseline policies devised for traditional CMOS-(/FinFET-) based caches are ineffective in improving NCFET-based LLC energy savings. Compared to the considered baseline policies, our CAPE policy also demonstrates better LLC energy-delay product (EDP) and throughput savings.

This work considers Maximum Likelihood Estimation (MLE) of a Toeplitz structured covariance matrix. In this regard, an equivalent reformulation of the MLE problem is introduced, and two iterative algorithms are proposed for the optimization of the equivalent statistical learning framework. Both strategies are based on the Majorization Minimization (MM) paradigm and hence enjoy nice properties such as monotonicity and ensured convergence to a stationary point of the equivalent MLE problem. The proposed framework is also extended to deal with MLE of other practically relevant covariance structures, namely, the banded Toeplitz, block Toeplitz, and Toeplitz-block-Toeplitz. Through numerical simulations, it is shown that the new methods provide excellent performance levels in terms of both mean square estimation error (which is very close to the benchmark Cramér-Rao Bound (CRB)) and signal-to-interference-plus-noise ratio, especially in comparison with state-of-the art strategies. Moreover, the estimation task is accomplished with a remarkable reduction in computational complexity compared with a standard approach relying on a Semidefinite Programming (SDP) solver.

This study introduces a self-powered terahertz (THz) detector based on a log periodic antenna- coupled arsenic telluride (α-As ₂ Te ₃ ), a low band gap pnictogen chalcogenides, operational at room temperature. The detector covers broad frequency range from 0.1 THz to 1.2 THz. At a frequency of 1.1 THz, the device exhibits a maximum responsivity of 248 mA/W, while achieving a detectivity of 0.25×10 ⁷ Jones at 0.2 THz, even in its self-powered configuration. Furthermore, applying bias enhanced the responsivity of the detector by approximately 2.2 times. A decent low noise equivalent power (NEP) of 9.7×10 ^-9 W-Hz ^-1/2 is achieved at room temperature. This antenna- coupled α-As ₂ Te ₃ based THz detection holds promise for future high- frequency applications in wireless communication and low-power electronics.

This letter presents an innovative approach based on Fourier-Bessel series expansion (FBSE) in order to identify seizure and normal electroencephalogram (EEG) signals. The coefficients obtained by applying FBSE are separated into the five EEG rhythms namely delta, theta, alpha, beta, and gamma rhythms are obtained by the different sets of FBSE coefficients. Further, images are generated from the matrices obtained after applying the concept of Euclidean distance on the EEG rhythms. The generated images are employed as features for the classification using convolutional neural network (CNN). Notably, our proposed methodology achieves 100% accuracy in distinguishing between seizure and normal EEG signals on the publicly available Bonn EEG dataset. This robust performance underscores the efficacy of our approach in handling complicated EEG signal patterns. The proposed framework for automated classification of epileptic seizure based on EEG rhythms provides information about the behaviour of rhythms during epilepsy. The experimental results on the publically available Bonn University EEG database are included to show the effectiveness of proposed framework. The performance of the proposed framework is also compared with the other existing frameworks from the literature

Pressure injuries cause discomfort and potential fatality, underscoring the importance of wound assessment. In the post-COVID era, remote monitoring of wounds, particularly through non-contact methods like infrared (IR) thermal imaging and deep learning, is imperative. This letter proposes a deep learning approach for dimension detection from thermal images, trained on data from 18 subjects. Instance segmentation achieved a maximum accuracy of 0.9542, with classification accuracy reaching 0.9922. The model exhibited a root mean square error (RMSE) of 0.1609cm for measured dimensions, with superior accuracy in detecting wound length (RMSE: 0.1114cm) compared to width (RMSE: 0.1506cm).

We have developed a perturbed relativistic coupled-cluster theory to study the parity nonconservation in atoms and ions. The method we have developed is quite robust and could be easily modified to compute other atomic properties just by changing the perturbation Hamiltonian. As benchmark test calculations, we have computed the electric dipole polarizability of ground and first excited state of Ar$^{13+}$. Further, as the clock transition property in Ar$^{13+}$, we have computed the M1 transition rate for $2p_{1/2} \rightarrow 2p_{3/2}$ transition. Our calculated results for both the properties are in very good agreement with the previous theoretical and experimental results.