"Pioneering Innovations in GIS and Remote Sensing"
Professor,
Sectional President, ICT Section, ISCA-2019,
Program Coordinator DST-FIST,
Department of Computer Science and IT,
Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS), INDIA.
Human Computer Interaction
HCI (human-computer interaction) is the study of how people interact with computers and to what extent computers are or are not developed for successful interaction with human beings. A significant number of major corporations and academic institutions now study HCI. Historically and with some exceptions, computer system developers have not paid much attention to computer ease-of-use. Many computer users today would argue that computer makers are still not paying enough attention to making their products "user-friendly." However, computer system developers might argue that computers are extremely complex products to design and make and that the demand for the services that computers can provide has always outdriven the demand for ease-of-use.
Research in this area focuses on developing more effective methods for humans to interact with and use computer technology. HCI draws from computer science, sociology, and psychology to create better interfaces, to improve human-human interactions, and to tailor computer technology to the needs of an individual or organization.
Remote Sensing & GIS
This specialization provides students with technical skills required to use state-of-the-art mapping technologies, such as geographic information systems (GIS), and cutting-edge data systems, such as those provided by satellite and aerial remote sensing and global positioning systems (GPS), for the analysis and presentation of environmental data.
Students master a large and diverse suite of technical tools in geospatial data analysis. These can be used to solve practical problems in watershed modeling, ecosystem science, wildlife ecology, water resource management, landscape ecology, pollution control, conservation biology, and land use/land cover dynamics.
Students take a core of basic and advanced suject detail in remote sensing and GIS, where they applied and use modern mapping technologies. Additional coursework provides them with practical instruction in how(Fieldspec-4) GIS and remote sensing are applied in environmental analysis and basic research.
Image Processing
Image processing is the use of computer algorithms to perform Image processing on images. Image processing has the same advantages over image processing as it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing.
Image processing operations can be roughly divided into three major categories, Image Compression, Image Enhancement and Restoration, and Measurement Extraction. Image compression is familiar to most people. It involves reducing the amount of memory needed to store a digital image. Image defects which could be caused by the digitization process or by faults in the imaging set-up (for example, bad lighting) can be corrected using Image Enhancement techniques. Once the image is in good condition, the Measurement Extraction operations can be used to obtain useful information from the image.
Data Mining
Big Data is becoming the business buzzword of the decade. The volume of data that is continually created is phenomenal, especially in the area of scientific research. Everyday, tera- and zettabytes of information are created by various industries. Life science companies have learned from the successful Human Genome Project how to filter this complex data and manage it in a way that augments their productivity. Other industries can take cues from life sciences organizations on how to best use complex data
One example is known as text-mining, where multiple data sources are filtered to identify word or text strings that may be linked. It assists researchers in making connections where connections did not even exist prior to the mining process. Further, taxonomies that assign different sets of data to separate classes are helpful in pushing data through yet another prism. Businesses that make the right investment in new semantic and taxonomical filtering technology will find a big difference in the ability of their employees to make the most of available data and help their organizations capitalize on their findings.
Digital Speech Processing
Speech recognition (SR) is the translation of spoken words into text. Speech recognition is used in electronics such as mobile phones, tablets, computers and industrial electronics. Speech recognition aims to eradicate the need for typing and increase usability. Speech recognition in a broader meaning refers to Speech-To-Text (STT) Translation, Text-To-Speech (TTS) Translation with speech synthesis (producing machine generated voice), and voice recognition which involves recognizing the speaker among a group of speakers.
Although Speech recognition software has been around for many years now, its accuracy is not enough for it to replace typing. Siri, Apple’s SR software lets you use your voice to send messages, schedule meetings, place phone calls and much more. Google and other leading electronic giants have their own SR software’s that allow you to do the same. Furthermore, Nuance Communications and many such companies produce SR software used in industries. Although these products have shown a pinch of hope for accurate SR devices, there is a lot to do!
SR falls under digital signal processing, a branch of electrical engineering. Although a speech recognition engineer is predominantly an electrical engineer, one has to be knowledgeable about many aspects of computer science, computer engineering, software engineering and also linguistics in some cases. An SR engineer applies knowledge of digital signal processing to create algorithms used in SR, to detect words spoken with uttermost accuracy. A SR engineer needs to be proficient in computer languages such as C++, C#, Java and scripting languages such as Perl, Python and even TCL (Tool Command Language) along with signals and systems, digital signal processing and analysis of algorithms.