Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.

     

Design and simulation of a novel GaN based resonant tunneling high electron mobility transistor on a silicon substrate

For the first time, we have introduced a novel GaN based resonant tunneling high electron mobility transistor (RTHEMT) on a silicon substrate. A monolithically integrated GaN based inverted high electron mobility transistor (HEMT) and a resonant tunneling diode (RTD) are designed and simulated using the ATLAS simulator and MATLAB in this study. The 10% Al composition in the barrier layer of the GaN based RTD structure provides a peak-to-valley current ratio of 2.66 which controls the GaN based HEMT performance. Thus the results indicate an improvement in the current-voltage characteristics of the RTHEMT by controlling the gate voltage in this structure. The introduction of silicon as a substrate is a unique step taken by us for this type of RTHEMT structure.     

Theoretical Study of Interplay of Superconductivity and Ferromagnetism in Hybrid Rutheno–Cuprate Superconductors RuSr2RCu2O8

We consider the extended two-band s–f model with additional terms, describing intersite Cooper pairs’ interaction between 4f (5f) and conduction electrons. Following Green’s function technique and equation of motion method, self-consistent equations for superconducting order parameter (Δ) and magnetic order parameter (m _f ) are derived. The expressions for specific heat, density of states, and free energy are also derived. The theory has been applied to explain the coexistence of superconductivity and ferromagnetism in hybrid rutheno-cuprate superconductors RuSr₂RECu₂O₈ (RE = Gd, Eu). The theory shows that it is possible to become superconducting if the system is already ferromagnetic. A study of specific heat, density of states and free energy is also presented. The agreement between theory and experimental observations is quite satisfactory.      

A call admission and control scheme for quality‐of‐service (QoS) provisioning in next generation wireless networks

We propose a framework for quality‐of‐service (QoS) provisioning for multimedia services in next generation wireless access networks. This framework aims at providing a differentiated treatment to multimedia traffic flows at the link layer, which can be broadly classified as real‐time (or delay‐sensitive) and non‐real‐time (or delay‐tolerant). Various novel schemes are proposed to support the differential treatment and guarantee QoS. These schemes include bandwidth compaction, channel reservation and degradation, with the help of which a call admission and control algorithm is developed. The performance of the proposed framework is captured through analytical modeling and simulation experiments. Analytically, the average carried traffic and the worst case buffer requirements for real‐time and non‐real‐time calls are estimated. Simulation results show up to 21% improvement in the admission probability of real‐time calls and up to 17% improvement in the admission probability of non‐real‐time calls, when various call control techniques like bandwidth compaction are employed. Using our channel reservation technique, we observe a 12% improvement in the call admission probability compared to another scheme proposed in the literature. This revised version was published online in August 2006 with corrections to the Cover Date.     

Microscopic Theory of Organic Superconductors

A microscopic theory of organic superconductors based on the concept of partial electron dielectrization is developed from first principles. Self-consistent equations for the superconducting order parameter () and insulating order parameter (D) are derived using a Green's function technique and equation of motion method. The theory is applied to explain the experimental results in the two-dimensional organic superconductor k-(BEDT-TTF)₂ Cu(NCS)₂. The present model explains coexistence of spin density wave (SDW) state and superconductivity state in the system. The behavior of superconducting order parameter (), insulating order parameter (D), specific heat, density of state, free energy, and critical field is also studied for the system k-(BEDT-TTF)₂ Cu(NCS)₂.     

A 5-GHZ Low-Power Series-Coupled BiCMOS Quadrature VCO With Wide Tuning Range

This letter presents a novel quadrature voltage controlled oscillator (QVCO) implemented in a 47-GHz SiGe BiCMOS technology. The QVCO is a serially coupled LC VCO that utilizes SiGe heterojunction bipolar transistors for oscillation and metal oxide semiconductor field effect transistors for coupling. The SiGe BiCMOS QVCO prototype achieves about 14.6% tuning range from 4.3 to 5GHz. The phase noise of the QVCO is measured as -114.3 dBc/Hz at 2-MHz offset. The 5-GHz QVCO core consumes 6-mA current from a 3.3-V power supply and occupies 0.88mm² area     

GPU accelerated novel particle filtering method

In this paper, a graphics processor unit (GPU) accelerated particle filtering algorithm is presented with an introduction to a novel resampling technique. The aim remains in the mitigation of particle impoverishment as well as computational burden, problems which are commonly associated with classical (systematic) resampled particle filtering. The proposed algorithm employs a priori-space dependent distribution in addition to the likelihood, and hence is christened as dual distribution dependent (D3) resampling method. Simulation results exhibit lesser values for root mean square error (RMSE) in comparison to that for systematic resampling. D3 resampling is shown to improve particle diversity after each iteration, thereby affecting the overall quality of estimation. However, computational burden is significantly increased owing to few excessive computations within the newly formulated resampling framework. With a view to obtaining parallel speedup we introduce a CUDA version of the proposed method for necessary acceleration by GPU. The GPU programming model is detailed in the context of this paper. Implementation issues are discussed along with illustration of empirical computational efficiency, as obtained by executing the CUDA code on Quadro 2000 GPU. The GPU enabled code has a speedup of 3 and 4 over the sequential executions of systematic and D3 resampling methods respectively. Performance both in terms of RMSE and running time have been elaborated with respect to different selections for threads per block towards effective implementations. It is in this context that, we further introduce a cost to performance metric (CPM) for assessing the algorithmic efficiency of the estimator, involving both quality of estimation and running time as comparative factors, transformed into a unified parameter for assessment. CPM values for estimators obtained from all such different choices for threads per block have been determined and a final value for the chosen parameter is resolved for generation of a holistic effective estimator.     

Functionalized gold nanoparticles: a detailed in vivo multimodal microscopic brain distribution study

In the present study, the in vivo distribution of polyelectrolyte multilayer coated gold nanoparticles is shown, starting from the living animal down to cellular level. The coating was designed with functional moieties to serve as a potential nano drug for prion disease. With near infrared time-domain imaging we followed the biodistribution in mice up to 7 days after intravenous injection of the nanoparticles. The peak concentration in the head of mice was detected between 19 and 24 h. The precise particle distribution in the brain was studied ex vivo by X-ray microtomography, confocal laser and fluorescence microscopy. We found that the particles mainly accumulate in the hippocampus, thalamus, hypothalamus, and the cerebral cortex.     

Interplay Between Superconductivity and Antiferromagnetism in HoNi2B2C

A microscopic theory of interplay between superconductivity and antiferromagnetism in rare-earth nickel boride, HoNi₂B₂C is developed from first principles. Self-consistent equations for the superconducting order parameter Δ and magnetic order parameter Γ are derived using a Green’s function technique and an equation of motion method. The theory is applied to explain the experimental results in the antiferromagnetic superconductor HoNi₂B₂C. The present model explains the true coexistence of superconductivity and antiferromagnetism in this system. The behavior of the superconducting order parameter (Δ), the magnetic order parameter (Γ), the specific heat, the density of states, the free energy and critical field (H _c) is also studied for the system HoNi₂B₂C. Distinct features of the coexistence region are discussed. There is the convincing evidence that the theory is fully compatible with the key experiments.     

Role of Cannabidiol for Improvement of the Quality of Life in Cancer Patients: Potential and Challenges

There is currently a growing interest in the use of cannabidiol (CBD) to alleviate the symptoms caused by cancer, including pain, sleep disruption, and anxiety. CBD is often self-administered as an over-the-counter supplement, and patients have reported benefits from its use. However, despite the progress made, the mechanisms underlying CBD’s anti-cancer activity remain divergent and unclear. Herein, we provide a comprehensive review of molecular mechanisms to determine convergent anti-cancer actions of CBD from pre-clinical and clinical studies. In vitro studies have begun to elucidate the molecular targets of CBD and provide evidence of CBD’s anti-tumor properties in cell and mouse models of cancer. Furthermore, several clinical trials have been completed testing CBD’s efficacy in treating cancer-related pain. However, most use a mixture of CBD and the psychoactive, tetrahydrocannabinol (THC), and/or use variable dosing that is not consistent between individual patients. Despite these limitations, significant reductions in pain and opioid use have been reported in cancer patients using CBD or CBD+THC. Additionally, significant improvements in quality-of-life measures and patients’ overall satisfaction with their treatment have been reported. Thus, there is growing evidence suggesting that CBD might be useful to improve the overall quality of life of cancer patients by both alleviating cancer symptoms and by synergizing with cancer therapies to improve their efficacy. However, many questions remain unanswered regarding the use of CBD in cancer treatment, including the optimal dose, effective combinations with other drugs, and which biomarkers/clinical presentation of symptoms may guide its use.