Autonomous Mobile Vehicle based on RFID Technology using an ARM7 Microcontroller

Radio Frequency Identification (RFID) system is looked upon as one of the top ten important technologies in the 20th century. Industrial automation application is one of the key issues in developing RFID. Therefore, this paper designs and implements a RFID-based autonomous mobile vehicle for more extensively application of RFID systems. The microcontroller LPC2148 is used to control the autonomous mobile vehicle and to communicate with RFID reader. By storing the moving control commands such as turn right, turn left, speed up and speed down etc. into the RFID tags beforehand and sticking the tags on the tracks, the autonomous mobile vehicle can then read the moving control commands from the tags and accomplish the proper actions. Due to the convenience and non-contact characteristic of RFID systems, the proposed mobile vehicle has great potential to be used for industrial automation, goods transportation, data transmission, and unmanned medical nursing etc. in the future. Experimental results demonstrate the validity of the proposed mobile vehicle

Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be attributed to noise or randomness in data (aleatoric) and incorrect model inferences (epistemic). While model uncertainty can be reduced with more data or bigger models, aleatoric uncertainty is more intricate. This work proposes a novel approach that interprets data uncertainty estimated from a self-supervised task as noise inherent to the data and utilizes it to reduce aleatoric uncertainty in another task related to the same dataset via data augmentation. The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task. Our findings demonstrate the effectiveness of the proposed approach in significantly reducing the aleatoric uncertainty in the image segmentation task while achieving better or on-par performance compared to the standard augmentation techniques.Comment: Accepted in IEEE International Symposium on Biomedical Imaging (ISBI) 202

A study on clinico social impact of teenage pregnancy in a tertiary care hospital

Background: In India, teenage pregnancy is an important public-health problem, although the national policy of the Government of India advocates the minimum legal age of marriage for girls to be 18 years. Data of the National Family Health Survey (NFHS)-3 revealed that 16% of women, aged 15-19 years, have already started childbearing. Teenage pregnancies represent a high-risk group in reproductive terms because of the double burden of reproduction and growth. Complications of pregnancy and childbirth are the leading cause of mortality among girls aged 15-19 years in developing countries. Aim and objective of the study was to study the prevalence of teenage pregnancies and to study the clinic social impact of teenage pregnancies.Methods: The observational cross-sectional study was conducted in Government General Hospital, Guntur in the department of Obstetrics and Gynaecology over three Months from August to October 2018. All pregnant women coming to either OPD or directly to the labour room were included in the study group. History was taken and examination was done.Results: Among the 709 deliveries in the institute, 138 are teenage pregnancies contributing to 19.4%. Prevalence of anaemia in teenage mothers is as high as 63.7%, pregnancy induced hypertension contributing to 26.8% and abortions 9.4%. The neonatal outcome is poor in teenage mothers, low birth weight 20.2% contributing to the main morbidity.Conclusions: Teenage pregnancy is associated with an increased incidence of preeclampsia, eclampsia, preterm delivery, increased incidence of instrumental deliveries and lower segment caesarean sections due to cephalopelvic disproportion, neonatal complications, increased neonatal morbidity and mortality mainly due to low birth weight was noted in babies delivered to teenage mothers.

Understanding Calibration of Deep Neural Networks for Medical Image Classification

In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by providing insights into the model's certainty, identifying cases that require attention, and establishing trust in its predictions. Consequently, the significance of a well-calibrated model becomes paramount in the medical imaging domain, where accurate and reliable predictions are of utmost importance. While there has been a significant effort towards training modern deep neural networks to achieve high accuracy on medical imaging tasks, model calibration and factors that affect it remain under-explored. To address this, we conducted a comprehensive empirical study that explores model performance and calibration under different training regimes. We considered fully supervised training, which is the prevailing approach in the community, as well as rotation-based self-supervised method with and without transfer learning, across various datasets and architecture sizes. Multiple calibration metrics were employed to gain a holistic understanding of model calibration. Our study reveals that factors such as weight distributions and the similarity of learned representations correlate with the calibration trends observed in the models. Notably, models trained using rotation-based self-supervised pretrained regime exhibit significantly better calibration while achieving comparable or even superior performance compared to fully supervised models across different medical imaging datasets. These findings shed light on the importance of model calibration in medical image analysis and highlight the benefits of incorporating self-supervised learning approach to improve both performance and calibration.Comment: Accepted in Computer Methods and Programs in Biomedicine Journa

South Asians have elevated postexercise blood pressure and myocardial oxygen consumption compared to Europeans despite equivalent resting pressure

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Stroke mortality rate is higher in South Asians than in Europeans, despite equivalent or lower resting blood pressure (BP). Elevated recovery BP after exercise predicts stroke, independently of resting values. We hypothesized that South Asians would have adverse postexercise hemodynamics and sought explanations for this. METHODS AND RESULTS: A population-based sample of 147 European and 145 South Asian middle-aged men and women performed the Dundee 3-minute step test. Cardiovascular risk factors were measured. BP, heart rate, and rate-pressure product, a measure of myocardial oxygen consumption, were compared. With 90% power and 5% significance, we could detect a difference of 0.38 of a standard deviation in any outcome measure. Resting systolic BP was similar in South Asians (144 mm Hg) and Europeans (142 mm Hg) (P=0.2), as was exercise BP (P=0.4). However, recovery systolic BP at 3 minutes after exercise was higher in South Asians by 4.3 mm Hg (95% confidence interval [CI], 0.2 to 8.3 mm Hg; P=0.04). This effect persisted when adjusted for exercise BP and work effort (5.4 mm Hg [95% CI, 2.2 to 8.7 mm Hg; P=0.001]). Adjustment for baroreflex insensitivity and greater aortic stiffness in South Asians contributes greatly to attenuating this ethnic difference (1.9 mm Hg [95% CI, -0.9 to 4.6 mm Hg; P=0.4]). Similarly, rate-pressure product recovery after exercise was impaired in South Asians by 735 mm Hg/min (95% CI, 137 to 1334 mm Hg/min; P=0.02); again, adjustment for baroreflex insensitivity and aortic stiffness attenuated this difference (261 mm Hg/min [95% CI, -39 to 561 mm Hg/min; P=0.3]). CONCLUSION: Postexercise recovery of BP and rate-pressure product is impaired in South Asians compared to Europeans even though resting and exercise BP are similar. This is associated with the autonomic dysfunction and aortic stiffness in South Asians.The British Heart Foundation funded this project. Drs Chaturvedi, Kooner, John Chambers, and Hughes received support from the NIHR (UK National Institute for Health Research) Biomedical Research Centre. Dr Shore received support from the Peninsula NIHR Clinical Research Facility

Decreasing referrals to transient ischemic attack clinics during the COVID-19 outbreak: results from a multi-centre cross-sectional survey

Objective. The COVID-19 pandemic is having major implications for stroke care with a documented significant fall in hospital acute stroke admissions. We investigated whether COVID-19 has resulted in a decreased number of referrals to the Transient Ischemic Attack (TIA) clinics across the North West London region. Setting and Design. All the TIA clinical leads of the North West London region received an invitation by email to participate in an online survey in May 2020. The survey questionnaire aimed to assess the number of patients with suspected TIA consecutively referred to each of the TIA clinics of the North West London region between 1st March to 30th April 2020, the COVID-19 period, and between 1st March to 30th April 2019. Results. We had a response rate of 100%. During the COVID-19 period, the TIA clinics of the North West London region received 440 referrals compared to 616 referrals received between 1st March to 30th April 2019 with a fall in the number of the referrals by 28.6%. In April 2020 compared with April 2019, the number of the referrals declined by 40.1%. Conclusions. This multicentre analysis documented a significant reduction in the number of patients referred with suspected TIA to the specialised rapid access outpatient clinics in the North West London region during the COVID-19 pandemic. Future studies are needed to confirm our findings and to better characterise the incidence of cerebrovascular disease during the COVID-19 pandemic

Melt-Spun SiGe Nano-Alloys: Microstructural Engineering Towards High Thermoelectric Efficiency

Silicon-germanium (SiGe) alloys are prominent high-temperature thermoelectric (TE) materials used as a powering source for deep space applications. In this work, we employed rapid cooling rates for solidification by melt-spinning and rapid heating rates for bulk consolidation employing spark plasma sintering to synthesize high-performance p-type SiGe nano-alloys. The current methodology exhibited a TE figure-of-merit (ZT) approximate to 0.94 at 1123 K for a higher cooling rate of similar to 3.0 x 10(7) K/s. This corresponds to approximate to 88% enhancement in ZT when compared with currently used radioisotope thermoelectric generators (RTGs) in space flight missions, approximate to 45% higher than pressure-sintered p-type alloys, which results in a higher output power density, and TE conversion efficiency (eta) approximate to 8% of synthesized SiGe nano-alloys estimated using a cumulative temperature dependence (CTD) model. The ZT enhancement is driven by selective scattering of phonons rather than of charge carriers by the high density of grain boundaries with random orientations and induced lattice-scale defects, resulting in a substantial reduction of lattice thermal conductivity and high power factor. The TE characteristics of synthesized alloys presented using the constant property model (CPM) and CTD model display their high TE performance in high-temperature regimes along with wide suitability of segmentation with different mid-temperature TE materials