Random ambience using high fidelity images

Most of the secure communication nowadays mandates true random keys as an input. These operations are mostly designed and taken care of by the developers of the cryptosystem. Due to the nature of confidential crypto development today, pseudorandom keys are typically designed and still preferred by the developers of the cryptosystem. However, these pseudorandom keys are predictable, periodic and repeatable, hence they carry minimal entropy. True random keys are believed to be generated only via hardware random number generators. Careful statistical analysis is still required to have any confidence the process and apparatus generates numbers that are sufficiently random to suit the cryptographic use. In this underlying research, each moment in life is considered unique in itself. The random key is unique for the given moment generated by the user whenever he or she needs the random keys in practical secure communication. An ambience of high fidelity digital image shall be tested for its randomness according to the NIST Statistical Test Suite. Recommendation on generating a simple 4 megabits per second random cryptographic keys live shall be reported

Sound Scholarship: Scope, Purpose, Function and Potential of Phonorecord Archives

published or submitted for publicatio

The reality paradox: Authenticity, fidelity, and the real in Battlefield 4

This article examines how the ‘Battlefield’ (EA Games) series of games generates authenticity in its soundtrack both through a meticulous approach to modelling the physical world and through the appropriation of audio characteristics from our, typically mediated, experience of conflict. It goes on to examine how we might reconcile such ‘authentic’ audio with the more ludic features of the soundtrack, required to support gameplay, that are typically presented as inauthentic. The absence of these sounds during narrative-based sequences and the acceptance of them without negative impact on immersion during gameplay implies that these inauthentic sounds appear not to disrupt the immersive qualities of the ‘authentic’ but only when clearly positioned as ego-ludic (heard only by the player, non-spatialized and synthetic in quality) and only within the context of challenge-based sequences of the game

A discrete graph Laplacian for signal processing

In this thesis we exploit diffusion processes on graphs to effect two fundamental problems of image processing: denoising and segmentation. We treat these two low-level vision problems on the pixel-wise level under a unified framework: a graph embedding. Using this framework opens us up to the possibilities of exploiting recently introduced algorithms from the semi-supervised machine learning literature. We contribute two novel edge-preserving smoothing algorithms to the literature. Furthermore we apply these edge-preserving smoothing algorithms to some computational photography tasks. Many recent computational photography tasks require the decomposition of an image into a smooth base layer containing large scale intensity variations and a residual layer capturing fine details. Edge-preserving smoothing is the main computational mechanism in producing these multi-scale image representations. We, in effect, introduce a new approach to edge-preserving multi-scale image decompositions. Where as prior approaches such as the Bilateral filter and weighted-least squares methods require multiple parameters to tune the response of the filters our method only requires one. This parameter can be interpreted as a scale parameter. We demonstrate the utility of our approach by applying the method to computational photography tasks that utilise multi-scale image decompositions. With minimal modification to these edge-preserving smoothing algorithms we show that we can extend them to produce interactive image segmentation. As a result the operations of segmentation and denoising are conducted under a unified framework. Moreover we discuss how our method is related to region based active contours. We benchmark our proposed interactive segmentation algorithms against those based upon energy-minimisation, specifically graph-cut methods. We demonstrate that we achieve competitive performance

Finding Patterns in Biological Parameters

Changes or variation occur in physiological parameters of the body when a person is going through a tough time or he is extremely happy. These changes in physiological parameters can be used for detecting emotions. Emotional computing is a field of Human Computer Interaction(HCI) where we detect human emotions. Emotion recognition based on affective physiological changes is a pattern recognition problem, and selecting specific physiological signals is necessary and helpful to recognize the emotions. In this paper, we have discussed various research papers analysing that how emotions are detected from physiological signals using non-invasive methods. Developers use various Data Mining techniques for developing such results. Heart Rate Variability(HRV), Skin Temperature(ST), Blood Volume Pulse(BVP) are the main highlights as these are key parameters in Physiological signals