Guided / Co-Guided 26 PhDs and 152 M Tech / MCA/ M Sc / MS in the area of Geospatial Technology and Management corresponding to the following faculties:

Dr Raja Ramanna Distinguished Fellow, RCI/DRDO, Hyderabad
ivm@ieee.org

Engineers have a prominent role in the knowledge economy. Technology change in terms of technological convergence and knowledge management is a major engineering challenge. Innovation of new products and processes form the core of new knowledge.

The theme chosen for Engineer’s Day 2015 merriments “Engineering challenges for Knowledge Era” is projected to address these facets of technology. Tremendous developments are taking place in science, technology and engineering world over and the knowledge has become the innovative trade of national economics. Engineers have a pre-­- eminent role in the knowledge economy.

As such promoting R and D, nurturing entrepreneurship and succeeding in framing world-­- class merchandises, form major accomplishments in this regard. Further, interdisciplinary team efforts are needed to succeed in planning the benefits of knowledge economy. Technology provides the tools to help the engineers and the organization to develop and apply its knowledge capital. Arthur Andersen's Michael Stone explains it this way:

Technology allows people to collect, find, filter and distribute information far more rapidly than ever before. It is now possible to move large volumes of information quickly, and institutionalize what has always been an earlier informal and haphazard process.

Changing technologies and path breaking technological developments will have to be subjugated in all engineering disciplines. Technological change, as given in the Wikipedia, is the overall process of invention, innovation and diffusion of technology or processes. The technological change is the outcome of the engineering challenges consisting of a set of missions viz., invention, improvement and diffusion of technologies. These missions could further be explicated on the lines of content given in Wikipedia as follows

In short, technological change is perceived currently as an inevitable outcome and noticeable rejoinder for engineering challenges, which is centered around convergence of both superior and supplementary technologies.

Technological convergence is the tendency that as technology changes, different technological systems sometimes evolve toward performing similar tasks. For example the Digital convergence refers to the convergence of four diligences into one business, information technologies, telecommunication, consumer electronics and entertainment. Previously separate technologies such as voice (telephone), data, and video can now share resources and intermingle with each other synergistically. This is a unique example of technological convergence in other words an output requiring the engineering challenges. Specifically this involves the converging of previously distinct media such as telephone and data comunications into common interfaces on single devices, such as most smart phones which can make phone calls and search the web.

The technological convergence is one way or other manyntimes an unexpected off-shoot of technology forecast. Here it is worth quoting the Moor's law for better appreciating the concept of technological convergence visa vis engineering challenges. Gordon E. Moore, who was working as the director of research and development (R&D) at Fairchild Semiconductor, was asked to predict what was going to happen in the semiconductor components industry over the next ten years. His response was a brief article entitled, "Cramming more components onto integrated circuits" in the thirty-fifth anniversary issue of Electronics magazine, which was published on April 19, 1965. Dr Moore speculated that by 1975 it would be possible to contain as many as 65,000 components on a single quarter-inch semiconductor. His reasoning was a log-linear relationship between device complexity (higher circuit density at reduced cost) and time. At the 1975 IEEE International Electron Devices Meeting Moore revised the forecast rate Semiconductor complexity would continue to double annually until about 1980 after which it would decrease to a rate of doubling approximately every two years. He outlined several contributing factors for this exponential behavior .Simultaneous evolution to finer minimum dimensions and what Moore called "circuit and device cleverness"

Shortly after 1975, this was popularized as "Moore's law" stating that integrated circuits would double in performance every 18 months. Predictions of similar increases in computer power had existed years prior to this. It is reported that Intel offered US$ 10,000 some time during 2005, to purchase a copy of the original Electronics issue in which Moore's article appeared. Although Moore's law initially was made in the form of an observation and forecast the more widely it became accepted, the more it served as a goal for the industry. This drove both marketing and engineering departments of semiconductor manufacturers to focus enormous energy aiming for the specified increase in processing power that it was presumed one or more of their competitors would soon attain. In this regard, it may be viewed as a self-fulfilling prophecy.

While talking about knowledge prophecy the issues for acquiring knowledge are equally important and there are specific instances where problem based learning yielded significant results. This process could help to meet the engineers capable of meeting current challenges in a more effective manner. In problem solving, arriving at decisions based on prior knowledge and reasoning is important while in problem based learning, the process of acquiring new knowledge based on recognition of a need to learn is important. In problem based learning, small groups of engineers are presented with contextual situations and asked to define the problem, decide what skills and resources are necessary to investigate the problem and then pose possible solutions. PBL starts with the problems rather than with exposition of disciplinary knowledge. Students acquire knowledge skills and understanding through a staged sequence of problems presented in sequence. However, not all studies have found in favour of problem based learning.

People are investing in systems to capture, organize, and disseminate information, and then call it knowledge. But knowledge, by definition, cannot be converted into an object and given from one person to another without established flowing mechansims. Much of the confusion and disappointment concerning knowledge management stems from confusion between information and knowledge since not even KM experts link knowledge to action. There is no clarity. Knowledge only diffuses when there are learning processesThere is a discussion going on at the moment on Linked-­-In, about the definition of knowledge, with several people arguing that a definition of knowledge is fundamental to knowledge management. Here reproduce the quoted example of a map of mineral data, which is to be used to site a gold mine:

Each point on the map is a datum - a mineral sample point, with a location in space. The map itself is information; built up from the data points in such a way that it shows patterns which can be interpreted by a trained geologist. However, to interpret that map needs knowledge. Every one other than a geologist could not interpret it - The knowledge - the know-how, acquired through training and through experience- allows a mining geologist to interpret the map and come to a decision - to site a goldmine, to take more samples, or to declare the area worthless.

In this example, the data, the information and the knowledge come together to form a decision, but the ignorant person, the person with no knowledge, could never make that correct decision. As such the importance of knowledge management arises here and it is to be perceived as a successor to reengineering.

Currently, no universally accepted definition of knowledge management exists, but there are some basic concepts to be explored, and considered. Simply put, knowledge management undertakes to identify what is in essence a human asset buried in the minds and hard drives of engineers working in an organization. Knowledge management also requires a system that will allow the creation of new knowledge, a dissemination system that will reach every engineer, with the ability to package knowledge as value-added in products, services and systems. However, knowledge management goes far beyond the storage and manipulation of data, or even of information. There are two kinds of knowledge: tacit, which is hard to articulate, versus explicit knowledge, which can be expressed in words and numbers and can be easily communicated and shared in hard form, as scientific formulas, codified procedures, or universal principles. Tacit or unarticulated knowledge is more personal, experiential, context specific, and hard to formalize. This kind of knowledge is difficult to communicate or share with others, and is generally inside the heads of individuals and teams. Since knowledge may be an organization's only sustainable competitive advantage, it is very important to capture tacit knowledge and transfer it. It is the engieering challenge

Knowledge is considered as discussed in various artciles as available in cyberspace as intangible, dynamic, and difficult to measure, but without it no developmental engineering institution can sustain. In fact, flows of knowledge is an indicator of institution's capacity to learn. The knowledge economy generally could be considered as different from the customary economy in several key respects: the economics is not of scarcity, but rather of abundance, and unlike most resources that deplete when used, information and knowledge can be distributed, and actually grows through such synergy.

Indian space program and misssile programs are the best examples of knowledge economy through large scale pooling of knowledge grid through deployment of principles of knowledge mangement. Knowledge principles in which recognized attention is paid to what some researchers called the "knowledge grid." It is defined to contain four categories identified as follows:

It is quite posssible that these 4 become the major challeges that need to be addressed for meeting the challenges. In other words reuse of knowledge saves work, reduces communication costs, and allows a enginners to take on more challenges, then knowledge management is in place.

The US National Science Foundation announced about 3 years back 14 grand engineering challenges for the 21st century that, if met, would greatly improve how we live. But that's not all, it wants every one to rank them. The final choices fall into four themes that are essential for humanity to flourish, - sustainability, health, reducing vulnerability and joy of living, the group said. Visitors to grand challenge site (www.grandengineeringchallenges.org) have been voting on what they think are the greatest challenges. Tens of thousands of people voted. The results of that poll in he order of importance are reproduced below

A big idea comes along at just the moment when the technology exits to implement it. Many times innovation is a matter of timing. In other words most of the innovations are accidental. The observation of objects and phenomenon happening around lead to development of some of the very crucial technologies for example- steam engine, penicillin etc., There were few exceptions which had lot of lead time from the time of concept to the development of technologies. For example the idea of sending a man to the moon was proposed right when the progress of microchips made it possible to put computer guidance systems into the nose cone of a rocket. Charles Babbage published his paper about a sophisticated computer in 1837, but it took a hundred years to achieve the scores of technological advances needed. It took several decades for acceptance of Radio by public in USA. Same is case of computer. But internet and mobile phones penetrated very fast into the social fabric as pat of tools of knowledge economy. That mean invention based on current technologies for current requirements innovation helps in meeting the engineering challenges as well as knowledge economy. Currently, the goal is to see intelligent people using innovation to create knowledge out of information.

Technological infrastructure that can link people enterprise-wide, support collaboration, and allow people in the organization to access all global resources.

Information technology is most effective when it converts the tacit knowledge of an individual into explicit knowledge that is then accessible by all.

Engineers must think through their technological systems to ensure they have the capabilities needed for the 21st century because technology such as Intranets and advanced collaborative software have made knowledge management and knowledge economy possible.

Competitive success will be based less on how strategically physical and financial resources are allocated, and more on how strategically intellectual capital is managed-from capturing, coding and disseminating information, to acquiring new competencies through training and development, and to re-engineering engineering processes. In view of these trends, and recognizing that knowledge has great potential value, engineering challenges require the professional to embark on comprehensive knowledge management programs.

The move to a knowledge or information-based econmy, there is requirement of a top-notch knowledge management system to secure a competitive edge and a capacity for learning..

This article is preapred by freely quting some of the observations as mentioned by several business and technology knowledge management experts /authors on web. Some of the illustrative and related papers are given below for reference on the following topics which can be searched on google web.

Science and Technology Foresight Web Conference Geostrategics Synthesis Report Scenario Planning, Science and Technology, National Research and Societal Considerations What Does a Chief Knowledge Officer Do?

Acknowledgement; I thank the Chairman and Executive members of the Institution of Engineers AP State Centre for inviting me to deliver a talk on the theme of the Engineer's day celebration 2015. I deem it to be a privilege to participate in this event.

Dr Raja Ramanna Distinguished Fellow
Iyyanki@icorg.org       ivm@ieee.org

G Governance, Spatial statistics, geospatial analysis, health care, GIS applications, Remote sensing, health data, emergency management, disease mapping, hot spots, disease outbreaks, hospital services, GPS, Environment, Climate change

G Governance (Geospatial Governance) can possibly be viewed or defined as the utilization of geospatial technology to facilitate decision makers taking spatially enabled and knowledgeable decisions. Good governance delivers superior services across all areas of government. E-governance is a well-liked expression at present and involves utilization of tools of technology to transformation of government services and work. Increasingly, geospatial technology is found to have scope for facilitating many tasks of governance, from facilities management to land records to natural resources and disaster management. With increased use of geospatial technology, governance processes are bound to grow from e-governance to g-governance. Geospatial technology essentially provides a framework for integrated problem solving. It enables us to understand problems better because it presents issues spatially, in a more comprehensible manner. Geospatial technology finds applications in advocacy, natural resources management, infrastructure management, environment, risk assessment, hazard mapping, disaster management etc.. As such, getting Geospatial thematic/attribute layers and their integration is the key for creation of G Governance services in any specified sector, for example, health, natural resources, climate change or disaster management etc.,

Geographic Information System (GIS) is a set of hardware and software tools for the input, storage, management, display and analysis of  geographic and spatial data using any information that can be linked to a geographic location such as events, people, or environmental characteristics. The movement to G-Governance calls for provisioning geospatial layers to related programs such as the National Spatial Data Infrastructure, state SDIs and the National e-Governance Program.

Health being a geographic issue and related to geospatial analyses finds many solutions in geospatial technology and epidemiology. Geospatial epidemiology is defined as “the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors”. It is increasingly being used in many studies by combining the methods of epidemiology, statistics and geospatial technology. Many environmental conditions affect people's health. For example, the distribution of wetlands may affect the dispersion of malaria, while groundwater aquifer and the location of solid or hazardous waste dumping sites may impact the drinking water quality, which in turn affect the resident's health. The focus of health care system is the patient. GISs are applicable to the care of patients with both infectious and chronic diseases. GIS also can help in identifying disparities in treatment access and outcomes. Because geographic areas are often homogeneous by race, ethinicity, language, or economic status, looking at spatial patterns of disease or other health out comes and relating them to demographic patterns may uncover healthcare disparities. Geospatial analytic techniques, such as proximity estimations and cluster analysis, are built on statistical methods that incorporate distance and direction measurements to generate geospatially accurate maps and graphic reports. Disease clustering can be classified as temporal clustering, spatial clustering or space time clustering. SaTScan software is very useful software that can perform geographical surveillance of a disease, detect clusters and test whether these clusters are statistically significant or not.

Identifying risks from hazards and assessing a hospital or healthcare facility’s vulnerabilities to these risks is fundamentally about having right information and in many cases geospatial information. Ideally speaking it is imperative to have all hazards vulnerability assessment for a given area and the scope for application of geospatial technology to it is unlimited both in terms of extent of spatial and thematic coverage as well as cost effectiveness. The hospitals are first respondents in a disaster; it is in the interest of effective response for hospitals to participate in activities related to disaster management.

Use of disaster modeling software in the GIS predicts what geographic areas are vulnerable in an incident like the release of hazardous plume. Hazard vulnerability assessment is the process that identifies the internal and external risks of all hazards (natural, technological, human caused and hazardous materials related) most likely to affect facilities and the possibility of severity of impacts on response and recovery if they were to occur. By understanding the risk exposures, it should be possible for Government and other private/public organizations to develop adequate mitigation, preparedness and response and recovery actions for those risks, thus reducing the vulnerability and impact from several angles. All hospitals must also conduct hazard vulnerability assessment and in future it is likely to be accreditation requirement by Government {Hospital Accreditation Program}. This enables hospitals to identify potential emergencies that could affect demand for the hospital’s services or its ability to provide those services, the likelihood of those events occurring and consequences of those events. An advantage of application of geospatial technology in such assessments provides the scope to create “what if” scenarios for facilitating effective mitigation measures.

Many health related institutions globally have been appreciated for the innovative and wide-ranging uses of Geographic Information Systems (GISs) to address problems in health including facilities planning. Spatial decision analysis tools are useful to hospital administrators and clinical program designers to decide where to build new facilities, locate intervention programs, or allocate resources. Various criteria can be considered and combined using multiple criteria. The result is a ranking of geographic areas according to their suitability for the desired usage. Different criteria can be weighted for their importance in the decision-making process. The selection and weighting of criteria can be varied to see the impact on the suitability ranking. Geospatial analysis is a key component in the effective use of GISs in health care for data exploration, hypothesis testing, and modeling. This interactive process can test and model different scenarios and combinations of factors, using different weights for factors.

Decision support in health care delivery and emergency public health response has gained incredible importance during the last decade for various reasons. Disease outbreaks like dengue fever have certainly exposed the flaws in the ability of public health, animal control, emergency response and health care delivery units to share critical data resources in a timely and efficient manner. Most health data collected from the existing sources does not pertain to certain common norms, formats and lack reliable geographic representation. As such the interoperability is an issue for taking it forward. The desired outcome of the interoperability is to create a standard frame of reference that facilitates decision making and cooperation and timely interventions in the event of any crisis. Success with the use of GIS is dependent on the availability of geospatial data which refers to data that includes geographic elements, such as latitude and longitude, as well as accurate information in the form of metadata elements. Georeferenced data is collected by use of Global Positioning System (GPS) device or by geocoding a given thematic or any other type maps, a process by which an address is assigned geographic coordinates. In general, coordinates derived from GPS devices are more accurate, but geocoding technology is quickly catching up and represents a very cost-effective way to enable legacy data for use in GIS. In addition to system level interoperability, situation demands the data level exchanges and geospatial analysis should be possible.

Advanced research in Geospatial Public Health and geospatial epidemiology is expected to play a pivotal role in disease mapping, developing community health surveillance, health care networks, managing health resources, population density, socio‐demographics, health & human services etc.  It is expected that the project would achieve breakthrough, through development and validation of operational public Health GIS models, publication of research results, formulating the road map for future developments in healthcare GIS. It would also help in better decision making in public health sector in terms of identification of challenges in implementation, learning’s from implementers and stakeholders and emerging developments.  It is further envisaged that the integration of Geospatial analysis and modeling would strengthen the Health Management Information Systems. It would promote Socio‐economic, demographic, environmental overlay which would facilitate in Epidemiological Analysis, Managing Health and Human Services in the best possible manner. The following could be the broad outcomes of the national project.

Remote sensing sensors provide data covering different regions of the electromagnetic spectrum at different spatial and spectral resolutions. Spatial resolution represents the smallest resolvable area (e.g., pixel), while spectral resolution corresponds to the smallest wavelength that can be detected in the spectral measurement. High spatial resolution multispectral sensors, such as LISS IV, QuickBird, World View -2 and Geo Eye, provide data at high to very high spatial resolution but with limited spectral resolution. Presently available data from hyperspectral sensors, such as Hyperion, AVIRIS and CHRIS, have medium spatial resolution (ranging between 17-30 m) with rich spectral information. Both these high resolution multispectral and hyperspectral sensors have their own importance in identification and delineation of features. Although airborne hyperspectral sensors can give these kinds of high spatio-spectral images, they are very expensive and are not affordable by everyone. Hence, a fusion of hyperspectral and high spatial resolution multispectral images can increase interpretation capabilities and assist in improved target detection.

Image fusion can be defined as the process of dealing with data and information from multiple sources to achieve refined/improved information for decision making.  Many studies that have been conducted based on the fusion of multispectral and panchromatic data have highlighted the advantages and importance of fusion. However, while these multispectral and panchromatic fusions can improve the qualities of the output images in the spatial context, they cannot improve the spectral properties of the fused output. The fusion of hyperspectral images with high resolution multispectral images may result in an image with high spectral and spatial resolution, in which the qualities of both of the input images can be preserved. Thus, the former can support better delineation of features spectrally, while the latter can help in identification of features spatially.

Image fusion can be defined as the process of dealing with data and information from multiple sources to achieve refined/improved information for decision making.  Many studies that have been conducted based on the fusion of multispectral and panchromatic data have highlighted the advantages and importance of fusion. However, while these multispectral and panchromatic fusions can improve the qualities of the output images in the spatial context, they cannot improve the spectral properties of the fused output. The fusion of hyperspectral images with high resolution multispectral images may result in an image with high spectral and spatial resolution, in which the qualities of both of the input images can be preserved. Thus, the former can support better delineation of features spectrally, while the latter can help in identification of features spatially.

Prof. Dr. Muralikrishna V Iyyanki received the M.Tech and PhD degrees from the Indian Institute of Technology (IIT), Madras and Indian Institute of Science (IISc), Banglore respectively. He served as assistant professor in IIT Madras (1976-1979), senior scientist and Head of the Marine Applications Division, National Remote Sensing Agency, Indian Space Research Organisation (1979-1987), Professor and Founder Head of the Centre for Spatial Information Technology (CSIT) at Jawaharlal Nehru Technological (JNT) University (1990-2008), and Director of JNT University's Research and Development Centre (2005-2008). In addition, he has served as a guest scientist at Germany's German Space Research Institute (DLR) and GKSS Research Centre, and as visiting/adjunct/consulting professor at several institutes around the world, including Administrative Staff College of India, Bharatidasan University, Asian Institute of Technology, Jackson State University and Chiba University. He is a Fellow of various respected organisations, including Institute of Engineers, Institute of Surveyors, International Congress for Disaster Management, AP Academy of Sciences, Indian Geophysical Union and Bhoovigyan Vikas Foundation. He has published more than 50 papers in peer reviewed journals, and about 80 papers in national and international conferences, as well as guiding/co-guiding 26 PhD and 152 Master students.

At present, he is a Dr Raja Ramanna Distinguished Fellow at the Defence Research and Development Organisation (DRDO), India, and the National Coordinator for Geospatial Public Health, which is a National Networking Government of India Project. He is also an expert member in several national committees on disaster management, natural resources management, national policies on open data and geospatial technologies, and governing councils of academic institutions. His present research focuses are on hyperspectral remote sensing image classification and geospatial public health management system.

Remote sensing sensors provide data covering different regions of the electro-magnetic spectrum at different spatial and spectral resolutions, where the spatial resolution represents the smallest resolvable area (e.g. pixel) and the spectral resolution corresponds to the smallest wavelength that can be detected in the spectral measurement. The high spatial resolution multispectral sensors like LISS IV, QuickBird, World View -2, Geo eye etc provide data at high to very high spatial resolution but with a limited spectral resolution. The presently available data from hyperspectral sensors (like Hyperion, AVIRIS, CHRIS) have a medium spatial resolution (ranging between 17m – 30m) with rich spectral information. Both these high resolution multispectral and hyperspectral sensors have their own importance in identification and in delineation of the features. Although airborne hyperspectral sensors can give these kinds of high spatio spectral images, these are very expensive and are not affordable by everyone.

Hence a fusion of the hyperspectral and high spatial resolution multispectral images may allow increased interpretation capabilities and can assist in improved target detection. Image fusion can be defined as the process of  dealing with data and information from multiple sources to achieve refined/improved information for decision making.  Many studies were made based on the fusion of multispectral and the panchromatic data that have highlighted the advantages and the importance of fusion. However these multispectral and panchromatic fusions can improve the qualities of the output image in the spatial context, but cannot improve the spectral properties of the fused output  The fusion of hyperspectral images with high resolution multispectral images may result in an image with high spectral and spatial resolution in which the qualities of both of the input images could be preserved. Thus, the former can support better delineation of features spectrally while the later can help in identification of features spatially.

There are various image fusion techniques available which can be used for fusing multispectral and panchromatic datasets like simple averaging, Principal Component (PC) sharpening, Brovey transform, Gram Schmidt fusion, Color Normalized technique, ehler’s fusion, wavelet based fusion, High Pass Filtering (HPF) method etc., each technique having its own advantages and limitations. Hyperspectral images consist of hundreds of contiguous bands with very narrow bandwidth and high spectral information. This quality aids in identification of materials of the objects on the ground unlike the multispectral datasets which can identify the patterns and coverage of the objects. However inspite of its high spectral resolution, its moderate spatial resolution induces many mixed pixels in the image that leads to misclassifications in the classified mapNot all algorithms that are used for multispectral datasets are suitable for fusing the hyperspectral data with multispectral data. Only some of the transformation based algorithms like Intensity Hue Saturation (IHS), Wavelet decomposition, Neural Networks, Knowledge-based image fusion, Gram Schmidt (GS) technique, Color Normalized Transform (CNT) etc. can help in fusing the hyperspectral and multi spectral datasets. The normal multispectral classifiers are not suitable for classifying the hyperspectral images due to their high volume and data redundancy. Various improved classification techniques like spectral angle mapper, artificial neural networks, spectral unmixing, support vector machine etc were developed for classifying the hyperspectral images.