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"Ray, S."
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A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects
Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.
A wearable ultrasound patch monitors subjects in motion using machine learning and wireless electronics.
Journal Article
EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA
Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CART) and Support Vector Machine (SVM) for crop classification. High Resolution optical data, Sentinel-2, MSI (10 m) was used for crop classification in the Indian Agricultural Research Institute (IARI) farm for the Rabi season 2016 for major crops. Around 100 crop fields (~400 Hectare) in IARI were analysed. Smart phone-based ground truth data were collected. The best cloud free image of Sentinel 2 MSI data (5 Feb 2016) was used for classification using automatic filtering by percentage cloud cover property using the GEE. Polygons as feature space was used as training data sets based on the ground truth data for crop classification using machine learning techniques. Post classification, accuracy assessment analysis was done through the generation of the confusion matrix (producer and user accuracy), kappa coefficient and F value. In this study it was found that using GEE through cloud platform, satellite data accessing, filtering and pre-processing of satellite data could be done very efficiently. In terms of overall classification accuracy and kappa coefficient, Random Forest (93.3%, 0.9178) and CART (73.4%, 0.6755) classifiers performed better than SVM (74.3%, 0.6867) classifier. For validation, Field Operation Service Unit (FOSU) division of IARI, data was used and encouraging results were obtained.
Journal Article
A wearable cardiac ultrasound imager
Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients
1
–
4
. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness
5
–
11
, and existing wearable cardiac devices can only capture signals on the skin
12
–
16
. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.
Innovations in device design, material fabrication and deep learning are described, leading to a wearable ultrasound transducer capable of dynamic cardiac imaging in various environments and under different conditions.
Journal Article
Lie symmetry analysis for similarity reduction and exact solutions of modified KdV–Zakharov–Kuznetsov equation
In this paper, the Lie group analysis is used to carry out the similarity reduction and exact solutions of the
(
3
+
1
)
-dimensional modified KdV–Zakharov–Kuznetsov equation. This research deals with the similarity solutions of mKdV–ZK equation. The mKdV–ZK equation has been reduced into a new partial differential equation with less number of independent variables, and again using Lie group symmetry method, the new partial differential equation is reduced into an ordinary differential equation. We have obtained the infinitesimal generators, commutator table of Lie algebra, symmetry group, and similarity reduction for the mKdV–ZK equation. In addition to that, solitary wave solutions of the mKdV–ZK equation are derived from the reduction equation. Thus, we obtain some new exact explicit solutions of the
(
3
+
1
)
-dimensional mKdV–ZK equation which describes the dynamics of nonlinear waves in plasmas.
Journal Article
A highly responsive NH3 sensor based on Pd-loaded ZnO nanoparticles prepared via a chemical precipitation approach
The gas-detecting ability of nanostructured ZnO has led to significant attention being paid to the development of a unique and effective approach to its synthesis. However, its poor sensitivity, cross-sensitivity to humidity, long response/recovery times and poor selectivity hinder its practical use in environmental and health monitoring. In this context, the addition of noble metals, as dopants or catalysts to modify the ZnO surface has been examined to enhance its sensing performance. Herein, we report preparation of Pd-loaded ZnO nanoparticles via a chemical precipitation approach. Various Pd loadings were employed to produce surface-modified ZnO nanostructure sensors, and their resulting NH
3
sensing capabilities both in dry and humid environments were investigated. Through a comparative gas sensing study between the pure and Pd-loaded ZnO sensors upon exposure to NH
3
at an optimal operating temperature of 350 °C, the Pd-loaded ZnO sensors were found to exhibit enhanced sensor responses and fast response/recovery times. The influence of Pd loading and its successful incorporation into ZnO nanostructure was examined by X-ray diffraction, high resolution-transmission electron microscopy, and X-ray photoelectron spectroscopy. XPS studies demonstrated that in all samples, Pd existed in two chemical states, namely Pd° and Pd
2+
. The possible sensing mechanism related to NH
3
gas is also discussed in detail.
Journal Article
Recent progress on natural fiber hybrid composites for advanced applications: A review
Natural fibers, as replacement of engineered fibers, have been one of the most researched topics over the past years. This is due to their inherent properties, such as biodegradability, renewability and their abundant availability when compared to synthetic fibers. Synthetic fibers derived from finite resources (fossil fuels) and are thus, affected mainly by volatility oil prices and their accumulation in the environment and/or landfill sites as main drawbacks their mechanical properties and thermal properties surpass that of natural fibers. A combination of these fibers/fillers, as reinforcement of various polymeric materials, offers new opportunities to produce multifunctional materials and structures for advanced applications. This article intends to cover recent developments from 2013-up to date on hybrid composites, based on natural fibers with other fillers. Hybrid composites preparation and characterization towards their applicability in advanced applications and the current challenges are also presented.
Journal Article
Twist-assisted optoelectronic phase control in two-dimensional (2D) Janus heterostructures
Atomically thin two-dimensional (2D) Janus materials and their Van der Waals heterostructures (vdWHs) have emerged as a new class of intriguing semiconductor materials due to their versatile application in electronic and optoelectronic devices. Herein, We have invstigated most probable arrangements of different inhomogeneous heterostructures employing one layer of transition metal dichalcogenide, TMD (MoS
2
, WS
2
, MoSe
2
, and WSe
2
) piled on the top of Janus TMD (MoSeTe or WSeTe) and investigated their structural, electronic as well as optical properties through first-principles based calculations. After that, we applied twist engineering between the monolayers from 0
∘
→
60
∘
twist angle, which delivers lattice reconstruction and improves the performance of the vdWHs due to interlayer coupling. The result reveals that all the proposed vdWHs are dynamically and thermodynamically stable. Some vdWHs such as MoS
2
/MoSeTe, WS
2
/WSeTe, MoS
2
/WSeTe, MoSe
2
/MoSeTe, and WS
2
/MoSeTe exhibit direct bandgap with type-II band alignment at some specific twist angle, which shows potential for future photovoltaic devices. Moreover, the electronic property and carrier mobility can be effectively tuned in the vdWHs compared to the respective monolayers. Furthermore, the visible optical absorption of all the Janus vdWHs at
θ
= 0
∘
can be significantly enhanced due to the weak inter-layer coupling and redistribution of the charges. Therefore, the interlayer twisting not only provides an opportunity to observe new exciting properties but also gives a novel route to modulate the electronic and optoelectronic properties of the heterostructure for practical applications.
Journal Article