No abstract

On-board image compression has been a growing trend in most recent satellite missions. Since majority of satellite applications deal with imagery; compression of images due to limited on-board data storage mediums has become a necessity. The idea of treating satellite imageries as fractals and then encoding them provides an efficient way of conserving bandwidth and per-bit storage costs. Fractal encoding is characterized by slow encoding times which somehow had hindered its popularity in spite of its impressive compression ratio scaling many orders as compared to JPEG. In order to circumvent this handicap, Fractal compression is implemented using powerful GPUs (Graphical Processor Units) that are capable of reaching astronomical computing speeds of around 900 GFLOPS (Quadro Graphic Cards from Nvidia TM ) with internal memory bandwidth ranging to 100 GB/s. This astounding parallel capability is probed to be used on board systems, providing much needed boost for image compression. As the decoding part of compressed fractal images is almost instantaneous, this part can be handled without any specific hardware at the ground station level. Further, the issue of on-board data storage mechanisms is discussed with emphasis on use of HDD instead of SSD and flash memories. In sum, the prime aim is to provide a seamless image compression mechanism coupled with decompression at ground station level thus providing real-time streaming of satellite images from satellite to the ground. General TermsFractal Image Compression, Graphical Processing Unit (GPU), Satellite images, On-board systems, et. al.

In this paper, a new approach is proposed to predict the fractal behavior of a distributed network traffic. In this research, traffic traces are collected from a distributed network operated by NETRESEC an independent software vendor with focus on the network security field, network forensics and analysis of network traffic. A traffic analysis on packet, connection, protocol and application layers are taken into consideration. Apart from it, an investigation of self-similar and long-range dependent behavioral characteristic is made prior to the collection of traffic traces. Traffic prediction plays an important role in guaranteeing Quality of Service (QoS) in distributed networks due to the diversity of services in a realtime network application. Traffic prediction can be useful for dynamic routing, congestion control and prevention, autonomous traffic engineering, proactive management of the network etc. The forecasting methods can be broadly classified into two categories: linear prediction and nonlinear prediction models. Hence, the idea behind this research is to propose a Multiple Regression-booster equation based on the correlation structure to have a more accurate predicted traffic data result than using the later nonlinear prediction models involving Neural Networks. The traffic is sniffed and exported to NeuroSolutions builder, SPSS and then examined. Further, the exported and dissected traffic data is fed as input to train the neural network to let it predict the resultant fractal behavior of the distributed network traffic and an equation is proposed to derive the ultimate close network traffic prediction in SPSS.

Human behavior is a complex combination of emotions, upbringing, experiences, genetics, and evolution. Attempts to fathom it have been a long sought-after human endeavor, and it still remains a mystery when it comes to actually interpreting or deriving it. One such trait, free will, or an ability to non-deterministically act without any external motivation has been one such instinct which has remained an enigma as so far as when it comes to fully understanding its genesis. Two schools of thoughts prevail, and both have attempted to understand this elusive quality. One school that has a long history has been exploring from the perspective of metaphysics, while the other one interprets it using rational science that includes biology, computing, and neuroscience. With the advent of artificial neural networks (ANN), a beginning has been made to computationally represent the biological neural structure. Despite the ANN technology in its infancy especially when it comes to actually mimic the human brain, major strides are self-evident in the field of object recognition, natural language processing, and other fields. On the other end of the spectrum, persistent efforts to understand let alone simulate the biologically derived unpredictability in thoughts and actions is still a far cry. This work aims to identify the subtle connections or hints between the biological derived unpredictability and the ANNs. Even an infinitesimal progress in this domain shall open the flood gates for more emotive human-like robots, better human-machine interface, and an overall spin-off in many other fields including neuroscience. Keywords Free will • Artificial neural network • Consciousness 1 Unpredictability and Its Genesis The main tenet of unpredictability in each of the animal's action or behavior is its evolutionary process [1, 2]. It is widely noticed in both the flora and the fauna that those species which were not able to change and adapt, dwindled, and few of them

No abstract