Co-operative sensor localization using maximum likelihood estimation algorithm

In wireless sensor networks, self-localizing sensors are required in a wide variety of applications, from environmental monitoring and manufacturing logistics to geographic routing. In sensor networks which measure high-dimensional data, data localization is also a means to visualize the relationships between sensors’ high dimensional data in a low-dimensional display.This thesis considers both to be part of the general problem of estimating the coordinates of networked sensors. Sensor network localization is ‘cooperative’ in the sense that sensors work locally, with neighboring sensors in the network, to measure relative location, and then estimate a global map of the network.The choice of sensor measurement technology plays a major role in the network’s localization accuracy, energy and bandwidth efficiency, and device cost. This thesis considers measurements of time-of-arrival(TOA), received signal strength (RSS), quantized received signal strength (QRSS), and connectivity. I have taken the simulated data taking varity position of the sensor. From these different position the Cram´er-Rao lower bounds on the variance possible from unbiased location estimators are derived and studied. In this CRB calculation I have taken the RSS case only. Maximum Likelihood estimation algorithm is studied and applied for a particular node position

Distributed Estimation in Wireless Sensor Networks: Robust Nonparametric and Energy Efficient Environment Monitoring

Wireless sensor networks estimate some parameters of interest associated with the environment by processing the spatio-temporal data. In classical methods the data collected at different sensor nodes are combined at the fusion center(FC) through multihop communications and the desired parameter is estimated. However, this requires a large number of communications which leads to a fast decay of energy at the sensor nodes. Different distributed strategies have been reported in literature which use the computational capability of the sensor nodes and the estimated local parameters of the neighborhood nodes to achieve the global parameters of interest. However all these distributed strategies are based on the least square error cost function which is sensitive to the outliers such as impulse noise and interference present in the desired and/or input data. Therefore there is need of finding the proper robust cost functions which would be suitable for wireless sensor network in terms of communication and computational complexities. This dissertation deals with the development of a number of robust distributed algorithms based on the notion of rank based nonparametric robust cost functions to handle outliers in the (i) desired data; (ii) input data; (iii) in both input and desired data; and (iv) desired data in case of highly colored input data. Exhaustive simulation studies show that the proposed methods are robust against 50% outliers in the data, provide better convergence and low mean square deviation. Further this thesis deals with a real world application of energy efficient environment monitoring. A minimum volume ellipsoid is formed using distributed strategy covering those sensor nodes which indicate the event of interest. In addition a novel technique is proposed for finding the incremental path for regularly placed sensor nodes. It is shown mathematically that the proposed distributed strategy enhances the lifetime of the entire network drastically

A Unified Local–Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors

Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches

The role of a combination of N-acetylcysteine and magnesium sulfate as adjuvants to standard therapy in acute organophosphate poisoning: A randomized controlled trial

Background: Mortality in acute organophosphate (OP) poisoning remains high despite current standard therapy with atropine and oximes. Due to dose-limiting side effects of atropine, novel therapies are targeting other putative mechanisms of injury, including oxidative damage, to reduce atropine dosage. Objectives: N-acetylcysteine (NAC) and magnesium sulfate (MgSO4) have different mechanisms of actions and should act synergistically in OP poisoning. In this study, we wanted to evaluate whether this novel combination, used as an adjuvant to standard care, could improve clinical outcomes. Methods: The study was conducted in the Emergency Department and ICU of AIIMS Bhubaneswar (a tertiary care center and government teaching institute) between July 2019 and July 2021. Eighty-eight adult patients with history and clinical features of acute OP poisoning were randomly allocated (1:1) into two groups. The Study group received 600 mg NAC via nasogastric tube thrice daily for 3 days plus a single dose of 4 g Inj. MgSO4 IV on first day and the Control group received suitably matched placebo (double-blinding) – in addition to standard care in both the groups. The primary outcome measure was to compare the total dose of Inj. Atropine required (cumulative over the entire treatment duration) between the control group and the study group receiving NAC and MgSO4. The secondary outcome measures were lengths of ICU and hospital stays, need and duration of mechanical ventilation, the differences in BuChE activity, oxidative stress biomarkers – MDA and GSH levels, the incidences of adverse effects including delayed sequalae like intermediate syndrome and OPIDN, and comparison of mortality between the two groups. Results: Data from 43 patients in Control and 42 patients in Study group was finally analyzed. The baseline parameters were comparable. Total atropine requirements were lower in the Study group [175.33 ± 81.25 mg (150.01–200.65)] compared to the Control [210.63 ± 102.29 mg (179.15–242.11)] [Mean ± SD (95% CI)], but was not statistically significant. No significant differences in any of the other clinical or biochemical parameters were noted. Conclusion: The N-acetylcysteine and MgSO4 combination as adjuvants failed to significantly reduce atropine requirements, ICU/hospital stay, mechanical ventilatory requirements, mortality and did not offer protection against oxidative damage

Household secondary attack rate in mild COVID-19

Background: The coronavirus disease 2019 (COVID-19) pandemic has reached a staggering number of almost 280 million cases worldwide, with over 5.4 million deaths as of 29 December 2021. A further understanding of the factors related to the household spread of the infection might help to bring about specific protocols to curb such transmission. Objective: This study aims to find the secondary attack rate (SAR) and factors affecting SAR among the households of mild COVID-19 cases. Methods: An observational study was designed where data of patients admitted at All India Institute of Medical Sciences, New Delhi due to mild COVID-19 were collected, and outcome was noted after the discharge of the patient. Index cases who were the first in the household to have a positive infection only were included. Based on these data, the overall household SAR, factors related to the index case and contacts that affected transmissibility were noted. Results: A total of 60 index cases having contacts with 184 household members were included in the present study. The household SAR was measured to be 41.85%. At least one positive case was present in 51.67% households. Children below 18 years old had lower odds of getting a secondary infection compared to adults and elderly [odds ratio (OR) = 0.46, 95%CI = 0.22–0.94, p = 0.0383). An exposure period of more than a week was significantly associated with a higher risk of infection (p = 0.029). The rate of transmissibility drastically declined with effective quarantine measures adopted by the index case (OR = 0.13, 95%CI = 0.06–0.26, p < 0.00001). Symptomatic index cases contributed more to the SAR than asymptomatic primaries (OR = 4.74, 95%CI = 1.03–21.82, P = 0.045). Healthcare worker index cases had lower rates of spread (OR = 0.29, 95%CI = 0.15–0.58, P = 0.0003). Conclusion: The high SAR shows the household is a potential high-risk unit for transmissibility of COVID-19. Proper quarantine measures of all those exposed to the index case can mitigate such spread and lead to reduction of risk of COVID-19 within a household