A SURVEY OF MULTISPECTRAL IMAGE DENOISING METHODS FOR SATELLITE IMAGERY APPLICATIONS

In comparison with the standard RGB or gray-scale images, the usual multispectral images (MSI) are intended to convey high definition and anauthentic representation for real world scenes to significantly enhance the performance measures of several other tasks involving with computervision, segmentation of image, object extraction, and object tagging operations. While procuring images form satellite, the MSI are often prone tonoises. Finding a good mathematical description of the learning-based denoising model is a difficult research question and many different researchesaccounted in the literature. Many have attempted its use with the application of neural network as a sparse learned dictionary of noisy patches.Furthermore, this approach allows several algorithm to optimize itself for the given task at hand using machine learning algorithm. However, inpractices, a MSI image is always prone to corruption by various sources of noises while procuring the images. In this survey, we studied the pasttechniques attempted for the noise influenced MSI images. The survey presents the outline of past techniques and their respective advantages incomparison with each other

UNIVERSAL INFRASTRUCTURE OF M2M ENABLED INTER-CLOUD SERVICES FOR INTELLIGENT TRANSPORTATION SYSTEM

 The objective of this study is to develop the design of a generic infrastructure for on-demand applications for intelligent transport systems (ITS) in an urban area. The main idea of the study is to allow seamless service composition and consumption, but also to allow rapid deployment of new services through the pooling of different devices and access networks that may be owned and operated by different actors such as telecom operators, transportation service operators, governmental organizations, etc. This research serves the solution for the problem of interoperability between different devices, on the fly device reconfiguration and service discovery. Â

DEVELOPMENT OF CYBER-PHYSICAL SYSTEM FOR LASER-BASED CONTROLLED CLOUD SEEDING

This research encompasses of developing a cyber-physical system (CPS) i.e., a system which can control the physical process of cloud formation by utilizing the LIDAR technology to modulate its frequency while directed on aerosol cloud in order to control the movement of cloud, formation and precipitation in a test bed settings. This will enable studying the detailed effects of laser induced cloud nucleation and also in modeling the cloud behavior with respect to the feedback loop between induced affect through lasers upon the aerosol cloud. This involves utilization of advanced algorithms, image processing techniques to model cloud behavior and artificial intelligence to control the laser frequency modulation and pulse count, directed or incidence angle to understand the response between laser effects on aerosol cloud. Â

A DEVELOPMENT OF VON NEUMANN MACHINES WITH ARTIFICAL NEURO-GLIA NETWORK

Artificial Neuro–Glia Networks (ANGNs) are upcoming approach in soft computing wherein the effects biological counterpart of artificial glia cells are used to support pattern based growth mechanism in artificial neural network. In this study we present a mathematical model of such ANGNs to build a von neumann machine. This method will properly learn its parameters for increasing the growth of neural network which can be used for solving several scaling problems in computing. Â

NEUROCOMPUTATIONAL MODELLING OF DISTRIBUTED LEARNING FROM VISUAL STIMULI

Neurocomputational modeling of visual stimuli can lead not only to identify the neural substrates of attention but also to test cognitive theories ofattention with applications on several visual media, robotics, etc. However, there are many research works done in cognitive model for linguistics,but the studies regarding cognitive modeling of learning mechanisms for visual stimuli are falling back. Based on principles of operation cognitivefunctionalities in human vision processing, the study presents the development of a computational neurocomputational cognitive model for visualperception with detailed algorithmic descriptions. Here, four essential questions of cognition and visual attention is considered for logicallycompressing into one unified neurocomputational model: (i) Segregation of special classes of stimuli and attention modulation, (ii) relation betweengaze movements and visual perception, (iii) mechanism of selective stimulus processing and its encoding in neuronal cells, and (iv) mechanism ofvisual perception through autonomous relation proofing

CENTRAL PROCESSING UNIT-GRAPHICS PROCESSING UNIT COMPUTING SCHEME FOR MULTI-OBJECT TRACKING IN SURVEILLANCE

This research work presents a novel central processing unit-graphics processing unit (CPU-GPU) computing scheme for multiple object trackingduring a surveillance operation. This facilitates nonlinear computational jobs to avail completion of computation in minimal processing time for tracking function. The work is divided into two essential objectives. First is to dynamically divide the processing operations into parallel units, and second is to reduce the communication between CPU-GPU processing units

MICROTUBULE BASED NEURO-FUZZY NESTED FRAMEWORK FOR SECURITY OF CYBER PHYSICAL SYSTEM

Network and system security of cyber physical system is of vital significance in the present information correspondence environment. Hackers and network intruders can make numerous fruitful endeavors to bring crashing of the networks and web services by unapproved interruption. Computing systems connected to the Internet are stood up to with a plenty of security threats, running from exemplary computer worms to impart drive by downloads and bot networks. In the most recent years these threats have achieved another nature of automation and sophistication, rendering most defenses inadequate. Ordinary security measures that depend on the manual investigation of security incidents and attack advancement intrinsically neglect to give an assurance from these threats. As an outcome, computer systems regularly stay unprotected over longer time frames. This study presents a network intrusion detection based on machine learning as a perfect match for this issue, as learning strategies give the capacity to naturally dissect data and backing early detection of threats. The results from the study have created practical results so far and there is eminent wariness in the community about learning based defenses. Machine learning based Intrusion Detection and Network Security Systems are one of these solutions. It dissects and predicts the practices of clients, and after that these practices will be viewed as an attack or a typical conduct

REGION SPECIFIC WAVELET COMPRESSION FOR 4K SURVEILLANCE IMAGES

For successful transmission of massively sequenced images during 4K surveillance operations large amount of data transfer cost high bandwidth, latency and delay of information transfer. Thus, there lies a need for real-time compression of this image sequences. In this study we present a region specific approach for wavelet based image compression to enable management of huge chunks of information flow by transforming Harr wavelets in hierarchical order.  Â

VIRTUAL FARMER: CONTROLLING PHYTOCHROME SIGNALING IN PLANTS THROUGH CYBER-PHYSICAL SYSTEM

Under external environment stimuli seedlings undergo variation of morphology and alterations in its genetic sequences. Phytochrome signaling i.e., feedback reaction of plants to photons and other nutrient cycle plays a crucial role in its maturation. In this research work we create a cyber physical system to control such morphogenesis of plants through the help of artificial intelligence framework which identifies and control the crucial feedback between plant's genetic transcription with respect to the external stimuli such as nutrients, electricity, magnetism. This leads to autonomously grow a plant without its disadvantageous traits by destabilizing its negatively acting transcriptional regulators and enhance the plant's advantageous features by controlling its positively acting transcriptional regulators. This has leaded us to control the plant metabolism, plant growth without soil, manipulate the immunity of plant against disease, develop a plant metabolic profile and maximizes its yield deprived off from its seasonal attribute.Â

PREDICTION OF CHRONIC BACTERIAL INFECTION BY IDENTIFICATION OF INTER CELLULAR RESPONSES OF GENETIC FUSION CENTERS

In the present study we have designed an algorithm for early detection of DNA  fusion to discover the potential transcription which embodies the fusion of  gene products  derivable from the human DNA with that of bacterial and cancerous viruses, resulting from the several breakage points and re-assembling of different chromosomes, or that of within a chromosome. Without relying on existing annotations the proposed algorithm proves its efficacy in detecting alignment of RNA sequences from unannotated splice variants of known genome strands. Using this algorithm in the age of Big Data analytics the potential threat of cancer, tuberculosis, tumors & asthma can be predicted beforehand while scaling such effects, ranging from individual to population scale. We have also reported the results of the algorithm for over 90 samples with solid supporting evidences and opens a new virotherapy approach of numerically quantized cure for disease like cancer, tumors & asthma.      Â