Inertial Navigation System Data Processing For Position Determination

Locating the positions and mapping the spatial information is of critical significance in the field of Precision Farming. Global Positioning System (GPS) is the main tool being utilized for this purpose but it is dependent on the satellite signals. Unfortunately these signals may get lost due to the blockage by canopy of the orchards or plantation. Inertial Navigation System (INS) can address this problem and support the non-availability of GPS signal for a short time. INS is capable of individually calculating the vehicle’s position without any external references. However, its high cost and time dependent errors are its major drawbacks. The research focuses on the mapping solution by INS only so that it can provide solution in the absence of GPS signal. Low cost inertial sensor (Xbow RGA300CA) was used for data collection and processing. Data Processing was done in Matlab/Simulink environment. A Simulink processing model is presented in detail to give an insight of the Strapdown INS Mechanization. Low pass filter and wavelet denoising model was used to assess the margin of improvement for noise filtering. Accurate GPS information was used as a reference of comparison. The model was tested in the lab as well as in the field for its validity. Before going to the field the Inertial sensor was tested in the lab for yaw rate drift and for stationary drift. For kinematic field testing, inertial sensor with GPS was mounted on the vehicle to get the positions for straight trajectories up to 100 meters. Results obtained are presented in detail. A gradual error growth was observed in the INS data and the sensor was found to be stable for short term only. Wavelet denoising was found to be better over short distances up to 40 meters while low pass filtering showed better performance over longer distances up to 100 meters

Investigations of Thermal Conversion and Kinetics of low-grade coal and bagasse using Thermogravemetric Technique.

In the present study thermal characterization of most abundantly available indigenous resources of low grade coal (Chamalang) and residue of agricultural waste (bagasse), and various Coal-bagasse blends (by weight) have been investigated, using thermogravimetric techniques. Proximate and ultimate analyses were also performed and heating values were measured. The operating parameters like heating rate (15, 20, & 40 0C/min), feed composition (85/15, 91/9 & 94/6), equivalence ratio (ER value 0.25,30 & 35) that affects the kinetics and thermal conversion process were tested using TGA in non-isothermal mode by maintaining sub-stoichiometric environment. TGA was operated from room temperatures to 950°C and it was observed that these parameters have very pronounced effects on the kinetics and conversion of these fuels. Coal-bagasse blends 91/9 (% w/w), heating rate of 20 0C/min and ER value 0.30 gives the optimum conversion. These parameters also had shown a pronounced effect on the activation energy that was determined by the regression analyses of weight loss data of TGA. .  TGA results contributed to the thermal characterization of low-grade coal, biomass as blended fuel for gasification. Therefore, understanding of proper thermochemical conversion processes for co-firing established the optimum operating conditions for the gasification reactor.  &nbsp

Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Brain-computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive signal processing and feature extraction. Deep learning (DL)-based convolutional neural networks (CNNs) recently achieved state-of-the-art electroencephalogram (EEG) signal classification accuracy. CNN models are complex and computationally intensive, making them difficult to port to edge devices for mobile and efficient BCI systems. For addressing the problem, a lightweight CNN architecture for efficient EEG signal classification is proposed. In the proposed model, a combination of a convolution layer for spatial feature extraction from the signal and a separable convolution layer to extract spatial features from each channel. For evaluation, the performance of the proposed model along with the other three models from the literature referred to as EEGNet, DeepConvNet, and EffNet on two different embedded devices, the Nvidia Jetson Xavier NX and Jetson Nano. The results of the Multivariant 2-way ANOVA (MANOVA) show a significant difference between the accuracies of ML and the proposed model. In a comparison of DL models, the proposed models, EEGNet, DeepConvNet, and EffNet, achieved 92.44 ± 4.30, 90.76 ± 4.06, 92.89 ± 4.23, and 81.69 ± 4.22 average accuracy with standard deviation, respectively. In terms of inference time, the proposed model performs better as compared to other models on both the Nvidia Jetson Xavier NX and Jetson Nano, achieving 1.9 sec and 16.1 sec, respectively. In the case of power consumption, the proposed model shows significant values on MANOVA (p < 0.05) on Jetson Nano and Xavier. Results show that the proposed model provides improved classification results with less power consumption and inference time on embedded platforms

Estimation of Producer gas from low-grade coal and biomass using Mathematical Modeling

Sustainable energy and reduction in greenhouse gases (GHG) are the challenging issues of the world. Due to the increase in globing warming concern, the industrial world has already reduced the use of fossil fuels to the status of minimal source of energy. Biomass is now believed to be an economically competitive with the fossil fuels, which can be subsequently used for the production of producer or syngas using appropriate technology of thermal conversion. The low energy contents of biomass are compensated by blending with a higher energy content coal. &nbsp;The major objective of this study was to better understand the effect of Temperature change on the producer gas composition in circulating fluidized bed gasifier (CFBG). Primarily the current work is experimental, but supplemented by the mathematical modeling that explains different steps in terms of existing scientific knowledge. The validation results showed&nbsp; that the model predicted the molar ratio of H2/CO in the producer gas in the range 0.97, 0.96, 0.93, 0.92,&nbsp; 0.927&nbsp; and&nbsp; the measured values&nbsp; 0.83, 0.79, 0.78, 0.73 and 0.74 at temperature degree (&deg;C) 750, 800, 820, 850 and 875&nbsp; respectively. Similarly the molar ratio of CO/CO2 predicted by the model was; 0.81, 0.84, 0.86, 0.89 and 0.85 against the experimental molar ratio 0.86, 0.91, 0.75, 0.66 and 0.59 respectively at fixed feed and ER value

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10&nbsp;years; 78.2% included were male with a median age of 37&nbsp;years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

How to teach ethics to the undergraduates in clinical context?

Teaching ethics to undergraduates is no more a part of hidden curriculum rather it is a norm to be incorporated in the formal curriculum. Regarding instructional strategies, the consensus is stagerry and mainly based on the institutional choices. To develop moral reasoning and address the social value system, Cognitive part is delivered through lecturing while affection part is discussed via reflective practices in small group settings through deliberate moral case teachings and portfolio note book writings. However the outcome of such practices is yet to be known. However we suggest that the ethical curriculum should base on the reflective practices, social constructivism and experiential learning. --- Continu

Experimental Study on Dry Torrefaction of Beech Wood

Torrefaction is a thermochemical pre-treatment process for upgrading the properties of biomass to resemble those of fossil fuels such as coal. Biomass properties of particular interest are chemical composition, physical property and combustion characteristics. In this work, torrefaction of beech wood and miscanthus (sinensis) was carried out to study the influence of torrefaction temperature (240–300 °C) and residence time (15–150 min) on the aforementioned properties of the biomass. Results of the study revealed that torrefaction temperature has a significant influence on mass and energy yields, whereas the influence of the residence time becomes more apparent for the higher torrefaction temperatures (>280 °C). Torrefied miscanthus resulted in higher energy densification compared to beech wood for a residence time of 30 min. A significant improvement in grindability of the torrefied beech wood was obtained even for lightly torrefied beech wood (at 280 °C and 15 min of residence time). Observation from the combustion study showed that the ignition temperature is slightly affected by the torrefaction temperature. As a whole, the torrefaction temperature determines the characteristics of the torrefied fuel compared to other process parameters like residence time. Furthermore, with optimal process conditions, torrefaction produces a solid fuel with combustion reactivity and porosity comparable to raw biomass, whereas grindability and heating value are comparable to low quality coal

Radial Heat Transfer Investigations in a Circulating Fluidized Bed Burning Makarwal Coal

In the present paper, a detailed parametric study is conducted on the radial heat transfer studies in a locally &nbsp;fabricated circulating fluidized bed combustor by burning Makarwal coal. The variations of radial heat transfer coefficients in a CFB combustor with different operating parameters are investigated. The experimental set up consists of a riser of a circulating fluidized bed (152 mm ID and 6 m high). A radial heat transfer probe (2" thick &amp; 15.4 cm ID) was used for these investigations. Silica sand of 2500 Kg/m3 and having average particle size of 125 &micro;m is used as bed material. A detail radial temperature profile was also investigated during the experimental runs. It was found that radial heat transfer coefficient increased with increase in suspension density, bed temperature and solid circulation rate

Regulation of Phosphorus and Zinc Uptake in Relation to Arbuscular Mycorrhizal Fungi for Better Maize Growth

Zinc (Zn) is an important micronutrient for plants, whose deficiency in alkaline soils creates hurdles in the achievement of optimum crop growth. Moreover, overuse of phosphorus (P) fertilizers often causes Zn immobilization in the soil. The employment of arbuscular mycorrhizal fungi (AMF) could be potentially environmentally friendly technology in this regard. Therefore, a pot experiment was conducted to assess the beneficial role of AMF (Glomus species) on maize under low and high P and Zn levels. Seven levels of Zn (0, 20, 40, 60, 80, 100 and 120 mg Zn kg−1 soil ZnSO4·7H2O) and three levels of P (0, 14.5, 29 and 58 kg ac−1 as single superphosphate) were applied with (M+) and without AMF (M−). The results showed that a high application rate of Zn (100 and 120 mg Zn kg−1 soil) restricted P translocation in plants and vice versa. Moreover, the nutritional status of mycorrhizal plants (AM) was better than non-mycorrhizal (NM) plants. AM plants showed a maximum positive response at 20 mg Zn kg−1 soil, or 29 kg P ac−1. In response to 20 mg Zn kg−1 soil, root colonization was maximum, which enhanced the maize nutrient concentration in shoots. In conclusion, AMF inoculation (M+) with P (29 kg ac−1) and Zn (20 mg kg−1) is efficacious for improving maize’s growth and nutrition. More investigations are suggested at the field level under different agroclimatic zones to ascertain whether P (29 kg ac−1) or Zn (20 mg kg−1) with AMF is the best treatment for maize growth optimization