Local stresses in plated structures

Imperial Users onl

Fabrication of silk-based composite scaffold for bone-ligament-bone graft using aqueous polymeric dispersion technique

Tissue engineering is a promising technology for treating tissue defects or replacing nonfunctional tissues/organs. It relies upon a temporary scaffold that is basically an artificial structure which provides the support for 3D tissue formation or organogenesis. Ideally, scaffolds should be able to accommodate human cells, orchestrate their growth and differentiation leading to tissue regeneration and ultimately make it feasible for implantation. Major sports injuries involve the damage of cartilages, ligaments, tendons and the enthesis. Since ligament injury is most common and ligament-alone grafts are not so successful to replace the injured ligaments, the researchers are experimenting with the construction of a composite scaffold which can guide the stem cells to differentiate into fibrocartilage that bridges of Bone-Ligament interface i.e. enthesis. In the current project, a composite silk-based scaffold was fabricated by incorporating multiple compartments for B-L-B graft. The core scaffold was prepared by knitting the silk fibers (from Bombyx mori) to provide required mechanical strength. The individual compartments over the knitted scaffold were coated with specific biocompatible components (i.e. hydroxyapatite for bone, Polyethylene oxide and & Polyethylene glycol for ligament and cartilage) blended with gelatin using Aqueous Polymer Dispersion (APD) Technique. The morphology of fabricated scaffolds was studied under optical microscope and SEM (Scanning Electron Microscope) while the mechanical properties were analysed through the Texture Analyzer. The particle sizes were found to be between 10-1000 nm. It was concluded that silk based multi-compartmental scaffolds fabricated from APD technique are suitable for enthesis tissue engineering due to their porosity and matching mechanical properties. However, the scaffolds need to be confirmed for their bioactivity by culturing live cells on respective compartment

Human Tracking and Activity Recognition for Surveillence Applications

Tracking and study of behavioural changes of human beings through vision is a challenging task. For surveillance, automated systems are important which can observe the traffic and can detect the abnormality. For tracking human or any kind of object, colour feature based mean shift technique is widely used. This technique uses Bhattacharya coefficient to locate the object based on the maximisation of the similarity function between object model and candidate model. Traditional mean shift algorithm fails when the object having large motion, occlusion, corrupted frames etc. In addition to that, the technique is not automatic to initiate the tracking. To overcome all these problems, this thesis work proposed a technique which uses three additional modules to the traditional method to make it more efficient. The proposed modules used human detection by modelling through star skeletonization, followed by block search algorithm and occlusion handling. Block search algorithm helped to supply an overlapping area to candidate model to continue the track when tracking fails due to fast motion. Occlusion handling helped in initiating the tracking after prolonged period of occlusion. The proposed method has been tested on real time data and it outperforms the conventional method effectively to overcome the mentioned problems up to large extent. Human activity recognition is a hierarchical procedure which confirms abnormality step by step. Low level activity recognition is a trajectory based application in which trajectory of tracks of a human being helps to detect the abnormal events like person fell down, illegal entry, abnormal loitering, line formation etc. At high level, human pose will be detected by the help of shape based human pose detection. The main aim of the system is to make a person independent real-time human activity recognition with decreased false alarm rates

RF Power Amplifier and Its Envelope Tracking

This dissertation introduces an agile supply modulator with optimal transient performance for the envelope tracking supply in linear power amplifiers. For this purpose, an on-demand current source module, the bang-bang transient performance enhancer (BBTPE), is proposed. Its objective is to follow fast variations in input signals with reduced overshoot and settling time without deteriorating the steady-state performance of the buck regulator. The proposed approach enables fast system response through the BBTPE and an accurate steady-state output response through a low switching ripple and power efficient dynamic buck regulator. Fast output response with the help of the added module induces a slower rise of inductor current in the buck converter that further assists the proposed system to reduce both overshoot and settling time. To demonstrate the feasibility of the proposed solution, extensive simulations and experimental results from a discrete system are reported. The proposed supply modulator shows 80% improvement in rise time along with 60% reduction in both overshoot and settling time compared to the conventional dynamic buck regulator-based solution. Experimental results for a PA using the LTE 16-QAM 5 MHz standard shows improvement of 7.68 dB and 65.1% in ACPR and EVM, respectively. In a polar power amplifier, the input signal splits into phase and amplitude components using a non-linear conversion operation. This operation broadens the spectrum of the polar signal components. The information of amplitude and phase contains spectral images due to the sampling operation in non-linear conversion operation. These spectral images can be large and cause out-of-band emission in the output spectrum. In addition, during the recombination process of phase and amplitude, a delay mismatch between amplitude and phase signals, which can occur due to separate processing paths of amplitude and phase signals, causes out-of-band emissions, also known as spectral regrowth. This dissertation presents solutions to both of the issues of digital polar power amplifier: spectral images and delay mismatch. In order to reduce the problem of spectral images, interpolation of phase and amplitude is proposed in this work. This increases the effective sampling frequency of the amplitude and phase, which helps to improve the linearity by around 10 dB. In addition, a novel calibration scheme is proposed here for the delay mismatch between phase and amplitude path in a digital polar power amplifier. The scheme significantly reduces the spectral regrowth. The scheme uses the same path for phase and amplitude delay calculation after the recombination that allows having a robust calibration. Furthermore, it can be executed during the empty transmission slots. The proposed scheme is designed in a 40 nm CMOS technology and simulated with a 64-QAM IEEE 802.11n wireless standard. The scheme achieved 7.57 dB enhancement in ACLR and 84.35% improvement in EVM for a 3.5 ns mismatch in phase and amplitude path

RF Power Amplifier and Its Envelope Tracking

This dissertation introduces an agile supply modulator with optimal transient performance for the envelope tracking supply in linear power amplifiers. For this purpose, an on-demand current source module, the bang-bang transient performance enhancer (BBTPE), is proposed. Its objective is to follow fast variations in input signals with reduced overshoot and settling time without deteriorating the steady-state performance of the buck regulator. The proposed approach enables fast system response through the BBTPE and an accurate steady-state output response through a low switching ripple and power efficient dynamic buck regulator. Fast output response with the help of the added module induces a slower rise of inductor current in the buck converter that further assists the proposed system to reduce both overshoot and settling time. To demonstrate the feasibility of the proposed solution, extensive simulations and experimental results from a discrete system are reported. The proposed supply modulator shows 80% improvement in rise time along with 60% reduction in both overshoot and settling time compared to the conventional dynamic buck regulator-based solution. Experimental results for a PA using the LTE 16-QAM 5 MHz standard shows improvement of 7.68 dB and 65.1% in ACPR and EVM, respectively. In a polar power amplifier, the input signal splits into phase and amplitude components using a non-linear conversion operation. This operation broadens the spectrum of the polar signal components. The information of amplitude and phase contains spectral images due to the sampling operation in non-linear conversion operation. These spectral images can be large and cause out-of-band emission in the output spectrum. In addition, during the recombination process of phase and amplitude, a delay mismatch between amplitude and phase signals, which can occur due to separate processing paths of amplitude and phase signals, causes out-of-band emissions, also known as spectral regrowth. This dissertation presents solutions to both of the issues of digital polar power amplifier: spectral images and delay mismatch. In order to reduce the problem of spectral images, interpolation of phase and amplitude is proposed in this work. This increases the effective sampling frequency of the amplitude and phase, which helps to improve the linearity by around 10 dB. In addition, a novel calibration scheme is proposed here for the delay mismatch between phase and amplitude path in a digital polar power amplifier. The scheme significantly reduces the spectral regrowth. The scheme uses the same path for phase and amplitude delay calculation after the recombination that allows having a robust calibration. Furthermore, it can be executed during the empty transmission slots. The proposed scheme is designed in a 40 nm CMOS technology and simulated with a 64-QAM IEEE 802.11n wireless standard. The scheme achieved 7.57 dB enhancement in ACLR and 84.35% improvement in EVM for a 3.5 ns mismatch in phase and amplitude path

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the \emph{a priori} settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs multilayer feedforward artificial neural network (ANN) with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network (CNN) fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. We detail the hyperparameters optimization of these models using the grid search algorithm

Frame invariant neural network closures for Kraichnan turbulence

Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial progress in developing parameterization (or closure) models for subgrid scale (SGS) processes using physical insights and mathematical approximations, they remain imperfect and can lead to inaccurate predictions. In recent years, machine learning has been successful in extracting complex patterns from high-resolution spatio-temporal data, leading to improved parameterization models, and ultimately better coarse grid prediction. However, the inability to satisfy known physics and poor generalization hinders the application of these models for real-world problems. In this work, we propose a frame invariant closure approach to improve the accuracy and generalizability of deep learning-based subgrid scale closure models by embedding physical symmetries directly into the structure of the neural network. Specifically, we utilized specialized layers within the convolutional neural network in such a way that desired constraints are theoretically guaranteed without the need for any regularization terms. We demonstrate our framework for a two-dimensional decaying turbulence test case mostly characterized by the forward enstrophy cascade. We show that our frame invariant SGS model (i) accurately predicts the subgrid scale source term, (ii) respects the physical symmetries such as translation, Galilean, and rotation invariance, and (iii) is numerically stable when implemented in coarse-grid simulation with generalization to different initial conditions and Reynolds number. This work builds a bridge between extensive physics-based theories and data-driven modeling paradigms, and thus represents a promising step towards the development of physically consistent data-driven turbulence closure models

Hematological Profile and Serum Potassium Level in Patients of Chronic Renal Failure at a Tertiary Health Care Center

Background: Chronic Kidney Disease (CKD) can be defined as an estimated glomerular Filtration Rate (eGFR) of less than ml/min/1.73 m2 for a minimum period of three months. CKD is commonly associated with various hematological abnormalities especially anemia. Aim: The present study was planned to assess the hematological variations in CKD patients as compared to healthy subjects. Method: Fifty patients diagnosed with CKD were enrolled for the study. Fifty age and sex-matched healthy subjects constituted the control group. Result: On comparison of the hematological profile, it was observed that all enrolled CKD patients were anemic with hemoglobin (Hb) less than 13g/dL in males and less than 12 g/dL in females. The mean Hb levels were as low as 7.50 + 1.55 g/dL (P< 0.0001). Correspondingly, the total RBC count of CKD patients was also low. It was also observed that the platelet count was slightly low among CKD patients. However, the mean level was comparable with the control group (P=NS). On further analysis, it was observed that among fifty CKD patients, 46% (n=23) suffered from severe anemia i.e. Hb < 7 gm/dL whereas 48% had moderate anemia i.e. Hb between 7-9.9 gm/dL. However, only 12 % (n=6) CKD patients suffered from thrombocytopenia i.e. platelets count < 1.50 lack/ cmm. Conclusion: Hematological abnormalities may lead to several associated morbidities and may pose a challenge for the maintenance of overall health status for CKD patients. Hence, there is a need to monitor the hematological profile of CKD patients especially those on dialysis so that any abnormality can be detected and managed accordingly. Keywords: Chronic kidney disease, Anemia, Hematological changes, Potassium, Platelets

Hematological Profile and Serum Potassium Level in Patients of Chronic Renal Failure at a Tertiary Health Care Center

Background: Chronic Kidney Disease (CKD) can be defined as an estimated glomerular Filtration Rate (eGFR) of less than ml/min/1.73 m2 for a minimum period of three months. CKD is commonly associated with various hematological abnormalities especially anemia. Aim: The present study was planned to assess the hematological variations in CKD patients as compared to healthy subjects. Method: Fifty patients diagnosed with CKD were enrolled for the study. Fifty age and sex-matched healthy subjects constituted the control group. Result: On comparison of the hematological profile, it was observed that all enrolled CKD patients were anemic with hemoglobin (Hb) less than 13g/dL in males and less than 12 g/dL in females. The mean Hb levels were as low as 7.50 + 1.55 g/dL (P< 0.0001). Correspondingly, the total RBC count of CKD patients was also low. It was also observed that the platelet count was slightly low among CKD patients. However, the mean level was comparable with the control group (P=NS). On further analysis, it was observed that among fifty CKD patients, 46% (n=23) suffered from severe anemia i.e. Hb < 7 gm/dL whereas 48% had moderate anemia i.e. Hb between 7-9.9 gm/dL. However, only 12 % (n=6) CKD patients suffered from thrombocytopenia i.e. platelets count < 1.50 lack/ cmm. Conclusion: Hematological abnormalities may lead to several associated morbidities and may pose a challenge for the maintenance of overall health status for CKD patients. Hence, there is a need to monitor the hematological profile of CKD patients especially those on dialysis so that any abnormality can be detected and managed accordingly. Keywords: Chronic kidney disease, Anemia, Hematological changes, Potassium, Platelets