43 research outputs found
Privacy-Preserving ECG Based Active Authentication (PPEA2) Scheme for Iot Devices
Internet of things (IoT) devices are becoming ubiquitous in, and even essential to, many aspects of day-to-day life, from fitness trackers, pacemakers, to industrial control systems. On a larger scale, live stream of sleep patterns data recorded via fitness tracker devices was utilized to quantify the effect of a seismic activity on sleep. While the benefits of IoT are undeniable, IoT ecosystem comes with its own set of system vulnerabilities that include malicious actors manipulating the flow of information to and from the IoT devices, which can lead to the capture of sensitive data and loss of data privacy. My thesis explores a Privacy-Preserving ECG based Active Authentication (PPEA2) scheme that is deployable on power-limited wearable systems to counter these vulnerabilities.
Electrocardiogram (ECG) is a record of the electrical activity of the heart, and it has been shown to be unique for every person. This work leverages that idea to design a feature extraction followed by an authentication scheme based on the extracted features. The proposed scheme preserves the privacy of the extracted features by employing a light-weight secure computation approach based on secure weighted hamming distance computation from an oblivious transfer. It computes a joint set between two participating entities without revealing the keys to either of them
Salinity and Toxicological Studies of Waters of Rajasthan Desert
Detailed studies on quality of ground waters of Western Rajasthan have been carried out by analysing about 1500 water samples for presence of total dissolved solids (TDS) and other normal chemical constituents. 109 ground water samples were tested for presence of 8 toxic substances viz. As, Ba, Cd, Cr/sup +6/, Pb, Se, Ag, and CN and F and NO/sub 3/. About 9 percent of the waters conform to the normal standards of drinking water i.e. contain less than 500 mg/l TDS. None of the water points has been found to be contaminated with toxic substances. However, fluoride and nitrate were present in all the samples.A survey of water-borne diseases, kidney diseases and fluorosis carried out to establish the possible correlation between prevailing diseases and dissolved solids in waters indicate that 82 percent of the reported cases are due to water-borne diseases. The guinea-worm (Dracunculus medinensis) has been found in the surface waters and sulphate reducing bacteria (Desulphovibrio desulphuricans) in the brackish water
Emergency ileo-cecal anastomosis with inclusion of appendicular stump in terminal ileal pathology: A newer approach
Background: In emergency settings, several surgical procedures are described while dealing with pathology of terminal ileal lying within 15 cm of the ileocecal valve, but there is still confusion and controversy over the optimal surgical treatment.Methods: A nonrandomized study of 210 patients with near terminal ileal pathology (within 15 cm) was carried out over a period of 10 years. The study included 112 cases in which an ileocecal anastomosis with inclusion of appendicular stump was used in terminal ileal pathologies, and in rest 98 cases, other surgical procedures were used. The outcomes were measured in relation to postoperative complications and mortality.Results: Postoperative complications encountered in emergency ileocecal anastomosis with the inclusion of appendicular stump were wound infection in 31 patients (34.72%), respiratory complications in 10 patients (11.2%), septicemia in 6 patients (6.72.%), and anastomotic leak in one patient (1.12%).Conclusion: The technique of ileocecal single‑layer anastomosis with the inclusion of appendicular stump was found to be very effective in dealing this common problem and had less morbidity and mortality.Keywords: Ileocecal anastomosis, multiple terminal ileal perforations, single layer anastomosis, terminal ileal gangren
Using Deep Learning to SegmentCardiovascular 4D Flow MRI : 3D U-Net for cardiovascular 4D flow MRI segmentation and Bayesian 3D U-Net for uncertainty estimation
Deep convolutional neural networks (CNN’s) have achieved state-of-the-art accuraciesfor multi-class segmentation in biomedical image science. In this thesis, A 3D U-Net isused to segment 4D flow Magnetic Resonance Images that include the heart and its largevessels. The 4 dimensional flow MRI dataset has been segmented and validated using amulti-atlas based registration technique. This multi-atlas based technique resulted in highquality segmentations, with the disadvantage of long computation times typically requiredby three-dimensional registration techniques. The 3D U-Net framework learns to classifyvoxels by transforming the information about the segmentation into a latent feature spacein a contracting path and upsampling them to semantic segmentation in an expandingpath. A CNN trained using a sufficiently diverse set of volumes at different time intervalsof the diastole and systole should be able to handle more extreme morphological differencesbetween subjects. Evaluation of the results is based on metric for segmentation evaluationsuch as Dice coefficient. Uncertainty is estimated using a bayesian implementationof the 3D U-Net of similar architecture.The presentation was online over zoom due to covid19 restrictions.</p
Aerosol particle deposition in fibrous media with dendritic pattern, comparison between theory and experiment
When a suspension of fine, solid particles in a gaseous medium flows through a fibrous filter, particles deposit on the fibers forming chainlike agglomerates known as dendrites. This deposition pattern is responsible for the intrinsically transient behavior of the filter, leading to drastic increases of the filter efficiency and of the pressure drop. In the present work, an experimental technique is developed to study the deposition of monodisperse aerosol on a single fiber under well defined and controlled experimental conditions. The same area on the fiber surface is examined after each deposition run. The angular position of the individual dendrites, their size and configuration are recorded and photographed. Thus, the growth of several individual dendrites is followed with the time of deposition. The filter parameters for the experiment were chosen so that interception with inertial impaction are the main deposition mechanisms. Other deposition mechanisms like Brownian motion, electrostatic and gravitational mechanisms do not contribute significantly to the deposition rate. The data thus obtained are then used to test the validity of the model developed by Payatakes (1977). [...]Chemical and Biomolecular Engineering, Department o
Using Deep Learning to SegmentCardiovascular 4D Flow MRI : 3D U-Net for cardiovascular 4D flow MRI segmentation and Bayesian 3D U-Net for uncertainty estimation
Deep convolutional neural networks (CNN’s) have achieved state-of-the-art accuraciesfor multi-class segmentation in biomedical image science. In this thesis, A 3D U-Net isused to segment 4D flow Magnetic Resonance Images that include the heart and its largevessels. The 4 dimensional flow MRI dataset has been segmented and validated using amulti-atlas based registration technique. This multi-atlas based technique resulted in highquality segmentations, with the disadvantage of long computation times typically requiredby three-dimensional registration techniques. The 3D U-Net framework learns to classifyvoxels by transforming the information about the segmentation into a latent feature spacein a contracting path and upsampling them to semantic segmentation in an expandingpath. A CNN trained using a sufficiently diverse set of volumes at different time intervalsof the diastole and systole should be able to handle more extreme morphological differencesbetween subjects. Evaluation of the results is based on metric for segmentation evaluationsuch as Dice coefficient. Uncertainty is estimated using a bayesian implementationof the 3D U-Net of similar architecture.The presentation was online over zoom due to covid19 restrictions.</p
Back Pain: Management & Controversies
Back pain is the second most frequent chronic pain problem, just after headache, as a reason for patients to seek medical help. Suffering from back pain ranges from transient discomfort to frank incapacitation. Approximately 80% of the adult population will suffer from pain related to the lower back during their lifetime. The Agency for Health Care Policy (AHCPR) document states that 90% of patients with acute back pain are better in about one month (spontaneous recovery), but more than half who recover from a first episode of acute pain will have another episode within a few years