2,874 research outputs found

    Determinants of neonatal mortality in rural India, 2007-2008.

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    Background. Despite the growing share of neonatal mortality in under-5 mortality in the recent decades in India, most studies have focused on infant and child mortality putting neonatal mortality on the back seat. The development of focused and evidence-based health interventions to reduce neonatal mortality warrants an examination of factors affecting it. Therefore, this study attempt to examine individual, household, and community level factors affecting neonatal mortality in rural India.Data and methods. We analysed information on 171,529 singleton live births using the data from the most recent round of the District Level Household Survey conducted in 2007–08. Principal component analysis was used to create an asset index. Two-level logistic regression was performed to analyse the factors associated with neonatal deaths in rural India.Results. The odds of neonatal death were lower for neonates born to mothers with secondary level education (O R = 0.60, p = 0.01) compared to those born to illiterate mothers. A progressive reduction in the odds occurred as the level of fathers’ education increased. The odds of neonatal death were lower for infants born to unemployed mothers (O R = 0.89, p = 0.00) compared to those who worked as agricultural worker/farmer/laborer. The odds decreased if neonates belonged to Scheduled Tribes (O R = 0.72, p = 0.00) or ‘Others’ caste group (O R = 0.87, p = 0.04) and to the households with access to improved sanitation (O R = 0.87, p = 0.02), pucca house (O R = 0.87, p = 0.03) and electricity (O R = 0.84, p = 0.00). The odds were higher for male infants (O R = 1.21, p = 0.00) and whose mother experienced delivery complications (O R = 1.20, p = 0.00). Infants whose mothers received two tetanus toxoid injections (O R = 0.65, p = 0.00) were less likely to die in the neonatal period. Children of higher birth order were less likely to die compared to first birth order.Conclusion. Ensuring the consumption of an adequate quantity of Tetanus Toxoid (TT) injections by pregnant mothers, targeting vulnerable groups like young, first time and Scheduled Caste mothers, and improving overall household environment by increasing access to improved toilets, electricity, and pucca houses could also contribute to further reductions in neonatal mortality in rural India. Any public health interventions aimed at reducing neonatal death in rural India should consider these factors

    Decomposing the gap in childhood undernutrition between poor and non–poor in urban India, 2005–06

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    Despite the growing evidence from other developing countries, intra-urban inequality in childhood undernutrition is poorly researched in India. Additionally, the factors contributing to the poor/non-poor gap in childhood undernutrition have not been explored. This study aims to quantify the contribution of factors that explain the poor/non-poor gap in underweight, stunting, and wasting among children aged less than five years in urban India.We used cross-sectional data from the third round of the National Family Health Survey conducted during 2005-06. Descriptive statistics were used to understand the gap in childhood undernutrition between the urban poor and non-poor, and across the selected covariates. Blinder-Oaxaca decomposition technique was used to explain the factors contributing to the average gap in undernutrition between poor and non-poor children in urban India.Considerable proportions of urban children were found to be underweight (33%), stunted (40%), and wasted (17%) in 2005-06. The undernutrition gap between the poor and non-poor was stark in urban India. For all the three indicators, the main contributing factors were underutilization of health care services, poor body mass index of the mothers, and lower level of parental education among those living in poverty.The findings indicate that children belonging to poor households are undernourished due to limited use of health care services, poor health of mothers, and poor educational status of their parents. Based on the findings the study suggests that improving the public services such as basic health care and the education level of the mothers among urban poor can ameliorate the negative impact of poverty on childhood undernutrition

    Maximum-Likelihood Sequence Detector for Dynamic Mode High Density Probe Storage

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    There is an increasing need for high density data storage devices driven by the increased demand of consumer electronics. In this work, we consider a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per in.2. The prevalent mode of using the cantilever probe is the static mode that is harsh on the probe and the media. In this article, the high quality factor dynamic mode operation, that is less harsh on the media and the probe, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state. We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes.Comment: This paper is published in IEEE Trans. on communicatio

    Performance evaluation for ML sequence detection in ISI channels with Gauss Markov Noise

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    Inter-symbol interference (ISI) channels with data dependent Gauss Markov noise have been used to model read channels in magnetic recording and other data storage systems. The Viterbi algorithm can be adapted for performing maximum likelihood sequence detection in such channels. However, the problem of finding an analytical upper bound on the bit error rate of the Viterbi detector in this case has not been fully investigated. Current techniques rely on an exhaustive enumeration of short error events and determine the BER using a union bound. In this work, we consider a subset of the class of ISI channels with data dependent Gauss-Markov noise. We derive an upper bound on the pairwise error probability (PEP) between the transmitted bit sequence and the decoded bit sequence that can be expressed as a product of functions depending on current and previous states in the (incorrect) decoded sequence and the (correct) transmitted sequence. In general, the PEP is asymmetric. The average BER over all possible bit sequences is then determined using a pairwise state diagram. Simulations results which corroborate the analysis of upper bound, demonstrate that analytic bound on BER is tight in high SNR regime. In the high SNR regime, our proposed upper bound obviates the need for computationally expensive simulation.Comment: This paper will appear in GlobeCom 201

    Collaborative Learning of Stochastic Bandits over a Social Network

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    We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies, and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network, and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks, and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive networkwide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results.Comment: 14 Pages, 6 Figure

    Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations

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    Size, weight, and power constrained platforms impose constraints on computational resources that introduce unique challenges in implementing localization algorithms. We present a framework to perform fast localization on such platforms enabled by the compressive capabilities of Gaussian Mixture Model representations of point cloud data. Given raw structural data from a depth sensor and pitch and roll estimates from an on-board attitude reference system, a multi-hypothesis particle filter localizes the vehicle by exploiting the likelihood of the data originating from the mixture model. We demonstrate analysis of this likelihood in the vicinity of the ground truth pose and detail its utilization in a particle filter-based vehicle localization strategy, and later present results of real-time implementations on a desktop system and an off-the-shelf embedded platform that outperform localization results from running a state-of-the-art algorithm on the same environment

    Decentralized spacing control with communication delay: a state space modeling based design approach

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    Spacing control is important topics of research as increasing number of vehicle are automatically controlled in land, space and under water. Spacing control has importance as it helps in safety of passengers and also saves time and fuel. It has application in many fields like aircraft flight formation vehicle platooning, spacecraft, etc. A platoon is group of vehicles travelling with a leader which is followed by the others. Information of the leader is very important in case of platoon to be safe. Vehicles are equipped with wireless communication which is used in sending and receiving information from the other vehicle and specially leader information. These communication systems are not always prefect, sometime or frequently it gets affected by environmental factors and is not able to send and receive information. The need of robust control law to minimize the distance between vehicles and that remains robust in uncertain conditions is important. In this research Switched Static Output Feedback Stabilization with LMI approach has been used. This method has advantages over earlier method that has limitation over switching sequence. Using SOF method gives us a simple control methodology that doesn’t need state information and LMI method is reliable in finding stability condition. It results in a control method that will stabilize the system in any arbitrary switching. System will attain string stability as it will ensure that error between vehicles doesn’t increases as we go down the platoon [1]
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