Pentagon Patch Antenna for WLAN at 2.6 GHz

In this paper an antenna with a pentagonal patch design is being prototype on FR-4 epoxy material fed by a microstrip line with a hexagonal parasitic element for bandwidth enhancement. A pentagon microstrip antenna with hexagonal  parasitic elements is designed and analyzed its properties for WLAN application at 2.52 GHz. The pentagon microstrip antenna with hexagonal parasitic element is being analyzed in terms of return loss, gain in dB and VSWR, etc. The experimental result shows a patch antenna is operating frequency at 2.52 GHz, with the return loss parameter is observed as -9.84 dB, and the VSWR ratio is < 2 respectively

Pentagon Patch Antenna for WLAN at 2.6 GHz

In this paper an antenna with a pentagonal patch design is being prototype on FR-4 epoxy material fed by a microstrip line with a hexagonal parasitic element for bandwidth enhancement. A pentagon microstrip antenna with hexagonal  parasitic elements is designed and analyzed its properties for WLAN application at 2.52 GHz. The pentagon microstrip antenna with hexagonal parasitic element is being analyzed in terms of return loss, gain in dB and VSWR, etc. The experimental result shows a patch antenna is operating frequency at 2.52 GHz, with the return loss parameter is observed as -9.84 dB, and the VSWR ratio is < 2 respectively

The social ecology of water in a Mumbai slum: failures in water quality, quantity, and reliability

Background: Urban slums in developing countries that are not recognized by the government often lack legal access to municipal water supplies. This results in the creation of insecure “informal” water distribution systems (i.e., community-run or private systems outside of the government’s purview) that may increase water-borne disease risk. We evaluate an informal water distribution system in a slum in Mumbai, India using commonly accepted health and social equity indicators. We also identify predictors of bacterial contamination of drinking water using logistic regression analysis. Methods: Data were collected through two studies: the 2008 Baseline Needs Assessment survey of 959 households and the 2011 Seasonal Water Assessment, in which 229 samples were collected for water quality testing over three seasons. Water samples were collected in each season from the following points along the distribution system: motors that directly tap the municipal supply (i.e., “point-of-source” water), hoses going to slum lanes, and storage and drinking water containers from 21 households. Results: Depending on season, households spend an average of 52 to 206 times more than the standard municipal charge of Indian rupees 2.25 (US dollars 0.04) per 1000 liters for water, and, in some seasons, 95% use less than the WHO-recommended minimum of 50 liters per capita per day. During the monsoon season, 50% of point-of-source water samples were contaminated. Despite a lack of point-of-source water contamination in other seasons, stored drinking water was contaminated in all seasons, with rates as high as 43% for E. coli and 76% for coliform bacteria. In the multivariate logistic regression analysis, monsoon and summer seasons were associated with significantly increased odds of drinking water contamination. Conclusions: Our findings reveal severe deficiencies in water-related health and social equity indicators. All bacterial contamination of drinking water occurred due to post-source contamination during storage in the household, except during the monsoon season, when there was some point-of-source water contamination. This suggests that safe storage and household water treatment interventions may improve water quality in slums. Problems of exorbitant expense, inadequate quantity, and poor point-of-source quality can only be remedied by providing unrecognized slums with equitable access to municipal water supplies

A System for Household Enumeration and Re-identification in Densely Populated Slums to Facilitate Community Research, Education, and Advocacy

Background: We devised and implemented an innovative Location-Based Household Coding System (LBHCS) appropriate to a densely populated informal settlement in Mumbai, India. Methods and Findings: LBHCS codes were designed to double as unique household identifiers and as walking directions; when an entire community is enumerated, LBHCS codes can be used to identify the number of households located per road (or lane) segment. LBHCS was used in community-wide biometric, mental health, diarrheal disease, and water poverty studies. It also facilitated targeted health interventions by a research team of youth from Mumbai, including intensive door-to-door education of residents, targeted follow-up meetings, and a full census. In addition, LBHCS permitted rapid and low-cost preparation of GIS mapping of all households in the slum, and spatial summation and spatial analysis of survey data. Conclusion: LBHCS was an effective, easy-to-use, affordable approach to household enumeration and re-identification in a densely populated informal settlement where alternative satellite imagery and GPS technologies could not be used

The high cost of diarrhoeal illness for urban slum households–a cost-recovery approach: a cohort study

Objectives: Rapid urbanisation has often meant that public infrastructure has not kept pace with growth leading to urban slums with poor access to water and sanitation and high rates of diarrhoea with greater household costs due to illness. This study sought to determine the monetary cost of diarrhoea to urban slum households in Kaula Bandar slum in Mumbai, India. The study also tested the hypotheses that the cost of water and sanitation infrastructure may be surpassed by the cumulative costs of diarrhoea for households in an urban slum community. Design: A cohort study using a baseline survey of a random sample followed by a systematic longitudinal household survey. The baseline survey was administered to a random sample of households. The systematic longitudinal survey was administered to every available household in the community with a case of diarrhoea for a period of 5 weeks. Participants: Every household in Kaula Bandar was approached for the longitudinal survey and all available and consenting adults were included. Results: The direct cost of medical care for having at least one person in the household with diarrhoea was 205 rupees. Other direct costs brought total expenses to 291 rupees. Adding an average loss of 55 rupees per household from lost wages and monetising lost productivity from homemakers gave a total loss of 409 rupees per household. During the 5-week study period, this community lost an estimated 163 600 rupees or 3635 US dollars due to diarrhoeal illness. Conclusions: The lack of basic water and sanitation infrastructure is expensive for urban slum households in this community. Financing approaches that transfer that cost to infrastructure development to prevent illness may be feasible. These findings along with the myriad of unmeasured benefits of preventing diarrhoeal illness add to pressing arguments for investment in basic water and sanitation infrastructure

The psychological toll of slum living in Mumbai, India: A mixed methods study

In India, “non-notified” slums are not officially recognized by city governments; they suffer from insecure tenure and poorer access to basic services than “notified” (government-recognized) slums. We conducted a study in a non-notified slum of about 12,000 people in Mumbai to determine the prevalence of individuals at high risk for having a common mental disorder (i.e., depression and anxiety), to ascertain the impact of mental health on the burden of functional impairment, and to assess the influence of the slum environment on mental health. We gathered qualitative data (six focus group discussions and 40 individual interviews in July-November 2011), with purposively sampled participants, and quantitative data (521 structured surveys in February 2012), with respondents selected using community-level random sampling. For the surveys, we administered the General Health Questionnaire-12 (GHQ) to screen for common mental disorders (CMDs), the WHO Disability Assessment Schedule 2.0 (WHO DAS) to screen for functional impairment, and a slum adversity questionnaire, which we used to create a composite Slum Adversity Index (SAI) score. Twenty-three percent of individuals have a GHQ score ≥5, suggesting they are at high risk for having a CMD. Psychological distress is a major contributor to the slum’s overall burden of functional impairment. In a multivariable logistic regression model, household income, poverty-related factors, and the SAI score all have strong independent associations with CMD risk. The qualitative findings suggest that non-notified status plays a central role in creating psychological distress—by creating and exacerbating deprivations that serve as sources of stress, by placing slum residents in an inherently antagonistic relationship with the government through the criminalization of basic needs, and by shaping a community identity built on a feeling of social exclusion from the rest of the city

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Compression of Deep Convolutional Neural Network Using Additional Importance-Weight-Based Filter Pruning Approach

The success of the convolutional neural network (CNN) comes with a tremendous growth of diverse CNN structures, making it hard to deploy on limited-resource platforms. These over-sized models contain a large amount of filters in the convolutional layers, which are responsible for almost 99% of the computation. The key question here arises: Do we really need all those filters? By removing entire filters, the computational cost can be significantly reduced. Hence, in this article, a filter pruning method, a process of discarding a subset of unimportant or weak filters from the original CNN model, is proposed, which alleviates the shortcomings of over-sized CNN architectures at the cost of storage space and time. The proposed filter pruning strategy is adopted to compress the model by assigning additional importance weights to convolutional filters. These additional importance weights help each filter learn its responsibility and contribute more efficiently. We adopted different initialization strategies to learn more about filters from different aspects and prune accordingly. Furthermore, unlike existing pruning approaches, the proposed method uses a predefined error tolerance level instead of the pruning rate. Extensive experiments on two widely used image segmentation datasets: Inria and AIRS, and two widely known CNN models for segmentation: TernausNet and standard U-Net, verify that our pruning approach can efficiently compress CNN models with almost negligible or no loss of accuracy. For instance, our approach could significantly reduce 85% of all floating point operations (FLOPs) from TernausNet on Inria with a negligible drop of 0.32% in validation accuracy. This compressed network is six-times smaller and almost seven-times faster (on a cluster of GPUs) than that of the original TernausNet, while the drop in the accuracy is less than 1%. Moreover, we reduced the FLOPs by 84.34% without significantly deteriorating the output performance on the AIRS dataset for TernausNet. The proposed pruning method effectively reduced the number of FLOPs and parameters of the CNN model, while almost retaining the original accuracy. The compact model can be deployed on any embedded device without any specialized hardware. We show that the performance of the pruned CNN model is very similar to that of the original unpruned CNN model. We also report numerous ablation studies to validate our approach