Studies on tuning surface electronic properties of hydrogenated diamond by oxygen functionalization

Ultra-wide bandgap and the absence of shallow dopants are the major challenges in realizing diamond based electronics. However, the surface functionalization offers an excellent alternative to tune electronic structure of diamonds. Herein, we report on tuning the surface electronic properties of hydrogenated polycrystalline diamond films through oxygen functionalization. The hydrogenated diamond (HD) surface transforms from hydrophobic to hydrophilic nature and the sheet resistance increases from ~ 8 kohms/sq. to over 10 Gohms/sq. with progressive ozonation. The conductive atomic force microscopic (c-AFM) studies reveal preferential higher current conduction on selective grain interiors (GIs) than that of grain boundaries confirming the surface charge transfer doping on these HDs. In addition, the local current conduction is also found to be much higher on (111) planes as compared to (100) planes on pristine and marginally O-terminated HD. However, there is no current flow on the fully O-terminated diamond (OD) surface. Further, X-ray photoelectron spectroscopic (XPS) studies reveal a redshift in binding energy (BE) of C1s on pristine and marginally O-terminated HD surfaces indicating surface band bending whilst the BE shifts to higher energy for OD. Moreover, XPS analysis also corroborate c-AFM study for the possible charge transfer doping mechanism on the diamond films which results in high current conduction on GIs of pristine and partially O-terminated HDs.Comment: 24 pages, 6 figures, 1 tabl

Slug-bubble regime identification in a square channel using a IR Sensor

Design of micro thrusters for nano satellites, require a detailed understanding of multiphase flow phenomena in micro/mini-channels. This work focuses on the experimental and numerical investigation of an Infra-red sensor behavior during two phase flow of a slug-bubble train (air-water two-phase flow). The regime flows inside a square channel of sides 2 mm and 0.5 mm thickness made of borosilicate glass. The interference of the slug-bubble train flow pattern on the IR transceiver characteristics is experimentally studied as current signals corresponding to the number of photons received by the photodiode. A numerical model is developed to analyze the IR transceiver characteristics using COMSOL Multiphysics package. The experimental and numerical results are in good agreement and the developed system with proper calibration can be used to design feedback loops for micro thrusters

Slug-bubble regime identification in a square channel using a IR Sensor

Design of micro thrusters for nano satellites, require a detailed understanding of multiphase flow phenomena in micro/mini-channels. This work focuses on the experimental and numerical investigation of an Infra-red sensor behavior during two phase flow of a slug-bubble train (air-water two-phase flow). The regime flows inside a square channel of sides 2 mm and 0.5 mm thickness made of borosilicate glass. The interference of the slug-bubble train flow pattern on the IR transceiver characteristics is experimentally studied as current signals corresponding to the number of photons received by the photodiode. A numerical model is developed to analyze the IR transceiver characteristics using COMSOL Multiphysics package. The experimental and numerical results are in good agreement and the developed system with proper calibration can be used to design feedback loops for micro thrusters

A Multi-Level Sensor-Based Spinal Cord Disorder Classification Model for Patient Wellness and Remote Monitoring

Detection of sagittal spine and spinopelvic disorder or injury is a key issue in children and adults due to variation in shape and orientation. Especially in remote patient monitoring for proper diagnose the structure and angular deviation of the lumbar spine and pelvis are required. So automatic rehabilitation process, especially for bedridden people at home, is very much essential for spinal cord injury patients to optimize the quick diagnosis process. The main objective of this work is to improve the classification rate of the vertebral spinal cord sensing data for automatic rehabilitation process. In this paper, a filter based multi-level segmentation and classification approach is implemented on the vertebral column dataset. In this approach, sensor generated spinal cord data are used in order to predict the severity level of each spinal cord disorder. In this approach, a novel vertebral data pre-processing method, a multi-level sensing approach and an improved random forest technique is proposed to predict the disorder with high true positive rate. Experimental results proved that the present model has better efficiency than the existing vertebral classifiers such as Naïve Bayes, Neural Network, Adaboost, Random forest and SVM in terms of true positive rate; TP = 0.9813, Accuracy = 0.9783, Error rate = 0.0246 are concerned

Empirical analysis of deep learning techniques for enhancing patient treatment facilities in healthcare sector

The study investigates with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to do so. Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. In a nutshell, deep learning is a subset of machine learning that solves problems that machine learning alone cannot. Deep learning use neural networks to boost computing labour while delivering accurate results. NLP, speech recognition, and facial recognition are just a few of the fantastic uses of deep learning. For example, when you submit a photo of yourself and a buddy to Facebook, Facebook dynamically tags your colleague and proposes a name for you to use. To recognise a face, Facebook employs deep learning algorithms. Deep learning techniques comprehend spoken human languages and transform them to text. Deep learning, in tandem with IoT, might lead to a slew of game-changing advancements in the future. Monitoring cardiac rhythms, as well as glucose levels, may be challenging, and even those who are represented at medical institutions. Intermittent heart rate assessments cannot protect against sudden changes in vital signs, and standard techniques of heart rhythm surveillance used in hospitals require patients to be permanently attached to wired apparatus, limiting their mobility