Academic literature on the topic 'Info Globe (Information retrieval system)'

Hospitals presently use a manual system for the management and maintenance of essential info. This system needs various paper forms, with knowledge stores unfold throughout the hospital management infrastructure. Usually, info (on forms) is incomplete or does not follow management standards. Forms are usually lost in transit between departments requiring a comprehensive auditing method to confirm that no important info is lost. Multiple copies of similar info exist within the hospital and should result in inconsistencies in knowledge in numerous knowledge stores. A significant part of the operation of any hospital involves the acquisition, management, and timely retrieval of nice volumes of knowledge. This info generally involves; patient personal info and case history, staff information and ward scheduling, scheduling programming, operating theatre scheduling, and numerous facilities waiting lists. All of this info should be managed in an economical and cost-wise fashion so that an institution's resources could also be effectively utilized. Patient info maintaining & Analyzing can automate the management of the hospital creating additional economic and the error free. It aims at standardizing data, consolidating data, reducing inconsistencies, and ensuring data integrity.

Content-based multimedia information retrieval is never a trivial task even with state-of-the-art approaches. Its mandatory challenge, called “semantic gap,” requires much more understanding of the way human perceive things (i.e., visual and auditory information). Computer scientists have spent thousands of hours seeking optimal solutions, only ended up falling in the bound of this gap for both visual and spoken contexts. While an over-the-gap approach is unreachable, we insist on assembling current viable techniques from both contexts, aligned with a domain concept base (i.e., an ontology), to construct an info service for the retrieval of agricultural multimedia information. The development process spans over three packages: (1) building a Vietnamese agricultural thesaurus; (2) crafting a visual-auditory intertwined search engine; and (3) system deployment as an info service. We spring our the thesaurus in 2 sub-boughs: the aquaculture ontology consists of 3455 concepts and 5396 terms, with 28 relationships, covering about 2200 fish species and their related terms; and the plant production ontology comprises of 3437 concepts and 6874 terms, with 5 relationships, covering farming, plant production, pests, etc. These ontologies serve as a global linkage between keywords, visual, and spoken features, as well as providing the reinforcement for the system performances (e.g., through query expansion, knowledge indexing…). On the other hand, constructing a visual-auditory intertwined search engine is a bit trickier. Automatic transcriptions of audio channels are marked as the anchor points for the collection of visual features. These features, in turn, got clustered based on the referenced thesauri, and ultimately tracking out missing info induced by the speech recognizer’s word error rates. This compensation technique bought us back 14 % of loss recall and an increase of 9 % accuracy over the baseline system. Finally, wrapping the retrieval system as an info service guarantees its practical deployment, asour target audiences are the majority of farmers in developing countries who are unable to reach modern farming information and knowledge.

Live repository of rose consisting of different genotypes and species of roses available across the globe has been established at ICAR-IIHR. All these genotypes have been characterized for 60 morphological characters for description of these varieties. Along with the live repository of plants, efforts have been made to develop digital repository of all these genotypes. The digital repository consists of description of characters, quantitative measurement for selected important characters and images for all the descriptors. A web-enabled interface has been developed for the selective retrieval of accessions with desired characters, and also for retrieval of all the information for the selected genotype. The information system will be useful across the germplasm collection centers, for the breeders and other end users by enabling them to select the appropriate germplasm andavoid duplicates.

The tourism statistics of Taiwan’s government state that the tourism industry is one of the fastest growing economic sources in the world. Therefore, the demand for a tourism information system with a friendly interface is growing. This research implemented the construction of a cloud information service platform based on numerous practical developments in the Dr. What-Info system (i.e., a master multi-agent system on what the information is), which developed universal application interface (UAI) technology based on the Taiwan government’s open data with the aim of connecting different application programming interfaces (APIs) according to different data formats and intelligence through local GPS location retrieval, in support of three-stage intelligent decision-making and a three-tier address-based UAI technology comparison. This paper further developed a novel citizen-centric multi-agent information monitoring and recommendation system for the tourism sector. The proposed system was experimentally demonstrated as a successful integration of technology, and stands as an innovative piece of work in the literature. Although there is room for improvement in experience and maybe more travel-related agents, the feasibility of the proposed service architecture has been proven.

Wireless Body Sensor Network with wearable and implantable body sensors have been grabbing lot of interests among the researchers and healthcare service providers. These sensors forward physiological data to the personnel at the hospital, doctor or caretaker anytime, anywhere; hence the name of the network is Ubiquitous health monitoring system. The technology has brought Internet of Things into this system making it to get connected to the cloud based internet. This has made the retrieval of information to the expert and thus improving the happiness of elderly people and patients suffering from chronic diseases. This paper focuses on creating an android based application for monitoring patients in hospital environment. The necessity of sharing hospital data to the experts around the globe has brought the necessity of trust in Health care systems. The data sharing in the IOT environment is secured. The environment is tested in real-time cloud environment. The proposed android application serves to be better architecture for hospital monitoring.

AbstractPre-2013, Shearman & Sterling employed only two full-time knowledge management (KM) professionals across the globe. As Jon Beaumont describes, there was no centralised method of storage or retrieval for knowledge and Attorneys would have to contend with searching the firm's Document Management System (DMS), SharePoint intranet, internal discussion boards or ten disparate knowledge systems for document and matter information. ‘Knowledge Center’ was launched in 2015, following two years of planning, aimed at consolidating firm systems and providing users with a single interface to access any required know-how. This article will touch upon the consolidation and migration of information, but focus predominantly on Knowledge Center itself, examining functionality, search, filtering and browse. Processes for better knowledge identification of both document and matter know-how, all of which have contributed to the success of Knowledge Center, shall also be considered.

Abstract. The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves vertical profiles of temperature, water vapor, greenhouse and pollutant gases, and cloud properties from measurements made by infrared and microwave instruments on polar-orbiting satellites. These are AIRS/AMSU on Aqua and CrIS/ATMS on Suomi NPP and NOAA20; together they span nearly 2 decades of daily observations (2002 to present) that can help characterize diurnal and seasonal atmospheric processes from different time periods or regions across the globe. While the measurements are consistent, their information content varies due to uncertainty stemming from (i) the observing system (e.g., instrument type and noise, choice of inversion method, algorithmic implementation, and assumptions) and (ii) localized conditions (e.g., presence of clouds, rate of temperature change with pressure, amount of water vapor, and surface type). CLIMCAPS quantifies, propagates, and reports all known sources of uncertainty as thoroughly as possible so that its retrieval products have value in climate science and applications. In this paper we characterize the CLIMCAPS version 2.0 system and diagnose its observing capability (ability to retrieve information accurately and consistently over time and space) for seven atmospheric variables – temperature, H2O, CO, O3, CO2, HNO3, and CH4 – from two satellite platforms, Aqua and NOAA20. We illustrate how CLIMCAPS observing capability varies spatially, from scene to scene, and latitudinally across the globe. We conclude with a discussion of how CLIMCAPS uncertainty metrics can be used in diagnosing its retrievals to promote understanding of the observing system and the atmosphere it measures.

Abstract. A fundamental goal of satellite weather and climate observations is profiling the atmosphere with in situ-like precision and resolution with absolute accuracy and unbiased, all-weather, global coverage. While GPS radio occultation (RO) has perhaps come closest in terms of profiling the gas state from orbit, it does not provide sufficient information to simultaneously profile water vapor and temperature. We have been developing the Active Temperature, Ozone and Moisture Microwave Spectrometer (ATOMMS) RO system that probes the 22 and 183 GHz water vapor absorption lines to simultaneously profile temperature and water vapor from the lower troposphere to the mesopause. Using an ATOMMS instrument prototype between two mountaintops, we have demonstrated its ability to penetrate through water vapor, clouds and rain up to optical depths of 17 (7 orders of magnitude reduction in signal power) and still isolate the vapor absorption line spectrum to retrieve water vapor with a random uncertainty of less than 1 %. This demonstration represents a key step toward an orbiting ATOMMS system for weather, climate and constraining processes. ATOMMS water vapor retrievals from orbit will not be biased by climatological or first-guess constraints and will be capable of capturing nearly the full range of variability through the atmosphere and around the globe, in both clear and cloudy conditions, and will therefore greatly improve our understanding and analysis of water vapor. This information can be used to improve weather and climate models through constraints on and refinement of processes affecting and affected by water vapor.

Abstract. Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of < 0.05 m3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

Abstract. Proven by multiple theoretical and practical studies, multi-angular spectral polarimetry is ideal for comprehensive retrieval of properties of aerosols. Furthermore, a large number of advanced space polarimeters have been launched recently or planned to be deployed in the coming few years (Dubovik et al., 2019). Nevertheless, at present, practical utilization of aerosol products from polarimetry is rather limited, due to the relatively small number of polarimetric compared to photometric observations, as well as challenges in making full use of the extensive information content available in these complex observations. Indeed, while in recent years several new algorithms have been developed to provide enhanced aerosol retrievals from satellite polarimetry, the practical value of available aerosol products from polarimeters yet remains to be proven. In this regard, this paper presents the analysis of aerosol products obtained by the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm from POLDER/PARASOL observations. After about a decade of development, GRASP has been adapted for operational processing of polarimetric satellite observations and several aerosol products from POLDER/PARASOL observations have been released. These updated PARASOL/GRASP products are publicly available (e.g., http://www.icare.univ-lille.fr, last access: 16 October 2018, http://www.grasp-open.com/products/, last access: 28 March 2020); the dataset used in the current study is registered under https://doi.org/10.5281/zenodo.3887265 (Chen et al., 2020). The objective of this study is to comprehensively evaluate the GRASP aerosol products obtained from POLDER/PARASOL observations. First, the validation of the entire 2005–2013 archive was conducted by comparing to ground-based Aerosol Robotic Network (AERONET) data. The subjects of the validation are spectral aerosol optical depth (AOD), aerosol absorption optical depth (AAOD) and single-scattering albedo (SSA) at six wavelengths, as well as Ångström exponent (AE), fine-mode AOD (AODF) and coarse-mode AOD (AODC) interpolated to the reference wavelength 550 nm. Second, an inter-comparison of PARASOL/GRASP products with the PARASOL/Operational, MODIS Dark Target (DT), Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol products for the year 2008 was performed. Over land both satellite data validations and inter-comparisons were conducted separately for different surface types, discriminated by bins of normalized difference vegetation index (NDVI): < 0.2, 0.2 ≤ and < 0.4, 0.4 ≤ and < 0.6, and ≥ 0.6. Three PARASOL/GRASP products were analyzed: GRASP/HP (“High Precision”), Optimized and Models. These different products are consistent but were obtained using different assumptions in aerosol modeling with different accuracies of atmospheric radiative transfer (RT) calculations. Specifically, when using GRASP/HP or Optimized there is direct retrieval of the aerosol size distribution and spectral complex index of refraction. When using GRASP/Models, the aerosol is approximated by a mixture of several prescribed aerosol components, each with their own fixed size distribution and optical properties, and only the concentrations of those components are retrieved. GRASP/HP employs the most accurate RT calculations, while GRASP/Optimized and GRASP/Models are optimized to achieve the best trade-off between accuracy and speed. In all these three options, the underlying surface reflectance is retrieved simultaneously with the aerosol properties, and the radiative transfer calculations are performed “online” during the retrieval. All validation results obtained for the full archive of PARASOL/GRASP products show solid quality of retrieved aerosol characteristics. The GRASP/Models retrievals, however, provided the most solid AOD products, e.g., AOD (550 nm) is unbiased and has the highest correlation (R ∼ 0.92) and the highest fraction of retrievals (∼ 55.3 %) satisfying the accuracy requirements of the Global Climate Observing System (GCOS) when compared to AERONET observations. GRASP/HP and GRASP/Optimized AOD products show a non-negligible positive bias (∼ 0.07) when AOD is low (< 0.2). On the other hand, the detailed aerosol microphysical characteristics (AE, AODF, AODC, SSA, etc.) provided by GRASP/HP and GRASP/Optimized correlate generally better with AERONET than do the results of GRASP/Models. Overall, GRASP/HP processing demonstrates the high quality of microphysical characteristics retrieval versus AERONET. Evidently, the GRASP/Models approach is more adapted for retrieval of total AOD, while the detailed aerosol microphysical properties are limited when a mixture of aerosol models with fixed optical properties are used. The results of a comparative analysis of PARASOL/GRASP and MODIS products showed that, based on validation against AERONET, the PARASOL/GRASP AOD (550 nm) product is of similar and sometimes of higher quality compared to the MODIS products. All AOD retrievals are more accurate and in good agreement over ocean. Over land, especially over bright surfaces, the retrieval quality degrades and the differences in total AOD products increase. The detailed aerosol characteristics, such as AE, AODF and AODC from PARASOL/GRASP, are generally more reliable, especially over land. The global inter-comparisons of PARASOL/GRASP versus MODIS showed rather robust agreement, though some patterns and tendencies were observed. Over ocean, PARASOL/Models and MODIS/DT AOD agree well with the correlation coefficient of 0.92. Over land, the correlation between PARASOL/Models and the different MODIS products is lower, ranging from 0.76 to 0.85. There is no significant global offset; though over bright surfaces MODIS products tend to show higher values compared to PARASOL/Models when AOD is low and smaller values for moderate and high AODs. Seasonal AOD means suggest that PARASOL/GRASP products show more biomass burning aerosol loading in central Africa and dust over the Taklamakan Desert, but less AOD in the northern Sahara. It is noticeable also that the correlation for the data over AERONET sites are somewhat higher, suggesting that the retrieval assumptions generally work better over AERONET sites than over the rest of the globe. One of the potential reasons may be that MODIS retrievals, in general, rely more on AERONET climatology than GRASP retrievals. Overall, the analysis shows that the quality of AOD retrieval from multi-angular polarimetric observations like POLDER is at least comparable to that of single-viewing MODIS-like imagers. At the same time, the multi-angular polarimetric observations provide more information on other aerosol properties (e.g., spectral AODF, AODC, AE), as well as additional parameters such as AAOD and SSA.

Wireless Body Sensor Network with wearable and implantable body sensors have been grabbing lot of interests among the researchers and healthcare service providers. These sensors forward physiological data to the personnel at the hospital, doctor or caretaker anytime, anywhere; hence the name of the network is Ubiquitous health monitoring system. The technology has brought Internet of Things into this system making it to get connected to the cloud based internet. This has made the retrieval of information to the expert and thus improving the happiness of elderly people and patients suffering from chronic diseases. This paper focuses on creating an android based application for monitoring patients in hospital environment. The necessity of sharing hospital data to the experts around the globe has brought the necessity of trust in Health care systems. The data sharing in the IOT environment is secured. The environment is tested in real-time cloud environment. The proposed android application serves to be better architecture for hospital monitoring.

Wireless Body Sensor Network with wearable and implantable body sensors have been grabbing lot of interests among the researchers and healthcare service providers. These sensors forward physiological data to the personnel at the hospital, doctor or caretaker anytime, anywhere; hence the name of the network is Ubiquitous health monitoring system. The technology has brought Internet of Things into this system making it to get connected to the cloud based internet. This has made the retrieval of information to the expert and thus improving the happiness of elderly people and patients suffering from chronic diseases. This paper focuses on creating an android based application for monitoring patients in hospital environment. The necessity of sharing hospital data to the experts around the globe has brought the necessity of trust in Health care systems. The data sharing in the IOT environment is secured. The environment is tested in real-time cloud environment. The proposed android application serves to be better architecture for hospital monitoring.

Agriculture activity monitoring needs to deal with large amount of data originated from various organizations (weather station, agriculture repositories, field management, farm management, universities, etc.) and mass people. Therefore, a scalable environment with flexible information access, easy communication and real time collaboration from all types of computing devices, including mobile handheld devices as smart phones, PDAs and iPads, Geo-sensor devices, and etc. are essential. It is mandatory that the system must be accessible, scalable, and transparent from location, migration and resources. In addition, the framework should support modern information retrieval and management systems, unstructured information to structured information processing (IBM Info Stream, text analytic, pig & hive, etc.), task prioritization, task distribution (Hadoop), workflow and task scheduling system, processing power and data storage (Amazon S3 and Google BigTable). Thus, High Scalability Computing (HSC) or Cloud based system can be a prominent and convincing solution for this circumstance.

Agriculture activity monitoring needs to deal with large amount of data originated from various organizations (weather station, agriculture repositories, field management, farm management, universities, etc.) and mass people. Therefore, a scalable environment with flexible information access, easy communication and real time collaboration from all types of computing devices, including mobile handheld devices as smart phones, PDAs and iPads, Geo-sensor devices, and etc. are essential. It is mandatory that the system must be accessible, scalable, and transparent from location, migration and resources. In addition, the framework should support modern information retrieval and management systems, unstructured information to structured information processing (IBM Info Stream, text analytic, pig & hive, etc.), task prioritization, task distribution (Hadoop), workflow and task scheduling system, processing power and data storage (Amazon S3 and Google BigTable). Thus, High Scalability Computing (HSC) or Cloud based system can be a prominent and convincing solution for this circumstance.

Traffic is an inevitable problem for metro cities around the globe. Intelligent traffic management system helps to improve the traffic flow by detecting congestions or incidents and suggesting appropriate actions on traffic routing. A new and dynamic internet-based decision-making tool for traffic management system was proposed and implemented in authors' previous works. The tool needs weather, road, and vehicle-related integrated information from different data repositories. Several online web portals host real-time weather data streams. However, road and vehicle information are missing in those portals. In addition, their coverage is limited to city-level congregate information but precise road segment-based information is necessary for real-time TMS decision. Internet of things (IoT)-based online sensors can be a solution for this circumstance. As a consequence, in this chapter, an IoT-based framework is proposed and implemented with several remote mobile agents. Agents are securely interconnected to the cloud, and able to collect and exchange data through wireless communication.