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.     

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.     

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.     

A polyphenolic biomacromolecule prepared from a flavonoid: Catechin as degradable microparticles

Catechin (CAT) was crosslinked with trimethylolpropane triglycidyl ether (TMPTGE) to obtain degradable poly(CAT) particles in a single step. Spherical p(CAT) particles with tens of micrometer size range and an isoelectronic point at pH 1.2 were obtained. The hydrolytic degradation of p(CAT) particles provided sustainable and extended release with 264 mg/g CAT release within 10 days at pH 7.4. The antioxidant capacity of 55.0 ± 0.9 μg/ml gallic acid equivalent in terms of total phenol content, and 0.88 ± 0.3 μmol/g trolox equivalent were estimated for p(CAT) particles displaying strong radical scavenging capability. Blood clotting and hemolysis assays demonstrated dose-dependent blood compatibility revealing higher blood compatibility for p(CAT) particles up to 10 μg/ml concentration. The cytotoxicity results show that p(CAT) particles have almost no toxicity for CCD841 normal colon cells at 250 μg/ml concentration in 24 h incubation time giving ~97% cell viability, whereas CAT molecules only provide ~34% cell viability.     

Ultra high gain CMOS Op-Amp design using self-cascoding and positive feedback

This paper presents an ultra high gain two stage CMOS Operational Amplifier which is designed using self-cascoding and positive feedback technique in order to provide gain enhancement. By comparing the circuit with other designed circuits it has been shown that applying positive feedback increases the gain of the Op-Amp without affecting other properties of the amplifier. The proposed circuit is designed in 45 nm technology using Cadence Virtuoso Analog Design Environment tool at ±1 V supply. The Op-Amp is designed to achieve a high gain of 141 dB while maintaining a UGB of 101 MHz and phase margin of 60°. The simulation results conforms the estimated theoretical improvements. The dependence of various properties such as slew rate, UGB, settling time and phase margin of the designed Op-Amp on compensating capacitor C_C has also been analyzed in this paper. Finally, the simulation results have been compared with a previously reported Op-Amp utilizing positive feedback technique.

     

Carbon Nanomaterial–Reinforced Epoxy Composites: A Review

Conducting fillers such as graphite, carbon nanotubes, and carbon nanofibers are used as reinforcing agents for enabling the conducting behavior of epoxy resin. The present review focused on the work related to epoxy composite with reinforcement of various nanofillers. Different methods of preparation for epoxy-based conducting nanocomposites are summarized. Various characterization techniques of epoxy composites such as Fourier transform infrared spectroscopy, Raman spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy are discussed. Important properties of epoxy-based nanocomposites are explained with different applications of epoxy-based conducting polymers.     

Enhanced I ON/I OFF in ultra deep submicron CMOS devices using grooved nMOSFETs in comparison to planar nMOSFET

To manage the increasing static leakage in low power applications and reducing ON‐OFF current ratio due to scaling limitations, solutions for leakage reduction as well as improving the current drive of the device are sought at the device design and process technology levels. At the device design level, the important low power variables are the threshold voltage, the gate leakage current, the subthreshold leakage current and the device size. Grooved‐gate MOS devices are considered as the most promising candidates for use in submicron and deep submicron regions as they can overcome the short‐channel effects effectively. By varying the corner angle and adjusting other structural parameters such as junction depth, channel doping concentration, negative junction depth and oxide thickness, leakage current in nMOS devices can be minimised. In this article, 90, 80, 70, 60 and 50?nm devices are simulated using Devedit and Deckbuild module of Silvaco device simulator. The simulated results show that by changing the structural parameters, ON‐OFF current ratio is improved and maintained constant even in the deep submicron region. This study can be helpful for low power applications as the static leakage is drastically reduced, as well as applicable to high speed devices as the ON current is maintained at a constant value. The results are compared with those of corresponding conventional planar devices to bring out the achievements of this study.