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1,197 results on '"Reddy, C. P"'

1. TunnelSense: Low-power, Non-Contact Sensing using Tunnel Diodes

Author

Thaddeus, Lim Chang Quan, Reddy, C. Rajashekar, Bhadauria, Yuvraj Singh, Shah, Dhairya, Gulati, Manoj, and Varshney, Ambuj

Subjects
Computer Science - Emerging Technologies
Abstract

Sensing the motion of physical objects in an environment enables numerous applications, from tracking occupancy in buildings and monitoring vital signs to diagnosing faults in machines. Typically, these application scenarios involve attaching a sensor, such as an accelerometer, to the object of interest, like a wearable device that tracks our steps. However, many of these scenarios require tracking motion in a noncontact manner where the sensor is not in touch with the object. A sensor in such a scenario observes variations in radio, light, acoustic, and infrared fields disturbed by the object's motion. Current noncontact sensing mechanisms often require substantial energy and involve complex processing on sophisticated hardware. We present TunnelSense, a novel mechanism that rethinks noncontact sensing using tunnel diode oscillators. They are highly sensitive to changes in their electromagnetic environments. The motion of an object near a tunnel diode oscillator induces corresponding changes in its resonant frequency and thus in the generated radio waves. Additionally, the low-power characteristics of the tunnel diode allow tags designed using them to operate on less than 100microwatt of power consumption and with a biasing voltage starting at 70 millivolt. This enables prolonged tag operation on a small battery or energy harvested from the environment. Among numerous applications enabled by the TunnelSense system, this work demonstrates its ability to detect breathing at distances up to 30 centimeter between the subject and the TunnelSense tag., Comment: This work is accepted at IEEE RFID 2024

Published
2024

28. Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network: An Indian Case Study

Author

Parmar, Ayu, Sara, Spanddhana, Dwivedi, Ayush Kumar, Reddy, C. Rajashekar, Patwardhan, Ishan, Bijjam, Sai Dinesh, Chaudhari, Sachin, Rajan, K. S., and Vemuri, Kavita

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatio-temporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization., Comment: Submitted to IEEE IoT Journal for review

Published
2022

34. Asymmetric rotations and dimerization driven by normal to modulated phase transition in 4-biphenylcarboxy coupled L-phenylalaninate

Author

Dey, Somnath, Sasmal, Supriya, Mondal, Saikat, Kumar, Santosh, Chowdhury, Rituparno, Sarkar, Debashrita, Reddy, C. Malla, Peters, Lars, Roth, Georg, and Haldar, Debasish

Subjects
Condensed Matter - Materials Science
Abstract

Amongst the derivatives of 4-biphenylcarboxylic acid and amino acid esters, the crystal structure of 4-biphenylcarboxy-(L)-phenylalaninate is unusual owing to its monoclinic symmetry within a pseudo-orthorhombic lattice. The distortion is described by disparate rotational property around the chiral centers ($\varphi_{\mathrm{chiral}}$ $\simeq$ -129 degrees and 58 degrees) of the two molecules in the asymmetric unit. Each of these molecules comprise of planar biphenyl moieties ($\varphi_{\mathrm{biphenyl}}$ = 0 degrees). Using temperature dependent single crystal X-ray diffraction experiments we show that the compound undergoes a phase transition below $T$ $\sim$ 124 K that is characterized by a commensurate modulation wave vector, $\mathbf{q}$ = $\delta(101)$, $\delta$ = $\frac{1}{2}$. The (3+1) dimensional modulated structure at $T$ = 100 K suggests that the phase transition drives the biphenyl moieties towards non coplanar conformations with significant variation of internal torsion ($\varphi^{\mathrm{max}}_{\mathrm{biphenyl}}$ $\leq$ $20$ degrees). These intramolecular rotations lead to dimerization of the molecular stacks that are described predominantly by intermolecular tilts and small variations in intermolecular distances. Atypical of modulated structures and superstructures of biphenyl and other polyphenyls, the rotations of individual molecules are asymmetric ($\Delta$$\varphi_{\mathrm{biphenyl}}$ $\approx$ 5 degrees) while $\varphi_{\mathrm{biphenyl}}$ of one independent molecule is two to four times larger than the other. Crystal-chemical analysis and phase relations in superspace suggest multiple competing factors involving intramolecular steric factors, intermolecular H--C${\cdot}{\cdot}{\cdot}$C--H contacts and weak C--H${\cdot}{\cdot}{\cdot}$O hydrogen bonds that govern the distinctively unequal torsional property of the molecules.

Published
2022
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47. A Transfer Learning Approach: Early Prediction of Alzheimer’s Disease on US Healthy Aging Dataset

Author

Kishor Kumar Reddy C, Aarti Rangarajan, Deepti Rangarajan, Mohammed Shuaib, Fathe Jeribi, and Shadab Alam

Subjects
Alzheimer’s disease (AD), VGG16, VGG19, Alex Net, dementia, neural networks, Mathematics, QA1-939
Abstract

Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. This work leveraged how the convolutional model and pooling layers work to improve and reduce overfitting, despite challenges in training the numerical dataset. VGG was preferably chosen as a hidden layer as it has a more diverse, deeper, and simpler architecture with better performance when dealing with larger datasets. It consumes less memory and training time. A comparative analysis was performed using machine learning and neural network algorithm techniques. Performance metrics such as accuracy, error rate, precision, recall, F1 score, sensitivity, specificity, kappa statistics, ROC, and RMSE were experimented with and compared. The accuracy was 100% for VGG16 and VGG19 and 98.20% for Alex Net. The precision was 99.9% for VGG16, 96.6% for VGG19, and 100% for Alex Net; the recall values were 99.9% for all three cases of VGG16, VGG19, and Alex Net; and the sensitivity metric was 96.8% for VGG16, 97.9% for VGG19, and 98.7% for Alex Net, which has outperformed when compared with the existing approaches for the classification of Alzheimer’s disease. This research contributes to the advancement of predictive knowledge, leading to future empirical evaluation, experimentation, and testing in the biomedical field.

Published
2024
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50. A Correlative Study of Vitamin D Receptor Variants Fok1, Apa1, Bsm1, and Taq1 Polymorphism and Vitamin D Deficiency with Higher Risk Ratio of Coronary Artery Disease in Female Population

Author

Shaik Mahaboob Vali, Madhuri S, Rohith Karnati, Kuragayala Swarna Deepak, Shaik Munni, Bhakthavatsala Reddy C, BabuLal S, and Gangapatnam Subrahmanyam

Subjects
coronary artery disease, vitamin d receptor, 1,25-dihydroxyvitamin d, gene polymorphism, Internal medicine, RC31-1245
Abstract

No studies are available on single nucleotide polymorphisms (SNPs) of the VDR gene in association with coronary artery disease (CAD) in women. The study aimed to investigate the association between SNPs in the VDR regions Fok I (rs10735810), Bsm I (rs1544410), Apa I (rs7975232), and Taq I (rs731236) and its associated risk of CAD in female patients. This prospective case-control study included 100 women diagnosed with CAD and age-matched healthy controls. The association between 1,25-dihydroxy vitamin D levels and VDR polymorph was assessed. In cases, the genotype ‘ff’ was reduced (14%) in co-dominant (OR, 0.13; CI, 0.03–0.65, p=0.0018) and recessive (OR, 0.11; CI, 0.02–0.54, p=0.0018) models. The ‘Ff’ genotype frequency in the over-dominant model was significantly higher (15%) in some cases (OR, 2.58; CI, 1.04–6.41, p=0.031). The frequency of genotype ‘Bb’ was increased (14%) in co-dominant (OR, 2.24; CI, 0.83–6.01, p=0.029) and over-dominant (OR, 2.65; CI, 1.01–6.94, p=0.035) cases. Women with CAD had a significantly lower frequency (14%) of the ‘bb’ genotype in both co-dominant (OR, 0.48; CI, 0.19–1.17, p=0.029) and recessive models (OR, 0.40; CI, 0.17–0.95, p=0.039). VDR genotypes such as Aa+aa, BB+Bb, Aa, Bb, FF+Ff, Tt+tt, aa, Ff, and tt were associated with an increased risk of CAD, and serum vitamin D levels were lower. Alleles ‘a’, ‘B’, and ‘t’ increase CAD risk in women. Subjects with genotypes (BB/Bb), (Tt/tt), and (FF) have a 2-fold, 2.6-fold, and 3-fold increased risk of low 25(OH)D3 levels. We found a statistically significant association between VDR polymorphisms and 25(OH)D3 levels for Taq I and Bsm I genotypes in females with CAD.

Published
2023
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