L’univers dans un point noir : esthétique et matérialité dans l’oeuvre de Jack Kirby

Notre recherche étudie les particularités esthétiques et matérialistes de l’oeuvre du bédéiste américain Jack Kirby (1917-1994) et la manière dont elles répondent d’agencements qui visent à déconstruire et reconstruire les formes sur la page. Contemporain de Will Eisner, Kirby est largement considéré comme l'auteur et dessinateur le plus influent de son époque, co-signant les premières aventures de certains des super-héros qui perdurent et qui sont aujourd’hui la manne de l’industrie hollywoodienne (Captain America, les Fantastic Four, Hulk, etc.). Son oeuvre protéiforme est composée de superpositions de textures, d’objets récupérés, de figures déviées et trouve dans son rapport à la matière les principaux axiomes qui la définissent. Cherchant dans son travail à cerner les fonctions des nombreux amoncellements de points noirs (baptisés kirby dots par la critique et l’industrie), nous nous écartons des modèles d’analyse sémiologiques pour constituer une approche écosophique de la bande dessinée. Dans cette dernière, nous avons recours à la schizo-analyse théorisée par Gilles Deleuze et Félix Guattari dans leur ouvrage L’Anti-OEdipe (1972) afin de cerner les conditions de la production de sens et de non-sens du point kirbyen. Pour ce faire, nous proposons de reconsidérer la BD comme une écologie séquentialisée, composée de cases sans icônes, c’est-à-dire d’un espace pris au plus près de la planche, pour soi et en soi, sans rapport de causalité fixe ou de structure prédéterminée. Nous envisageons ensuite les territorialités archaïques de la bande dessinée pour mieux définir son ontogénie, puis pour étudier les rapports machiniques et schizos qu’entretiennent entre eux les différents espaces (que nous distinguons en espaces striés et en espaces lisses) ainsi que les différents traits de la planche. Finalement, nous expliquerons en quoi le point kirbyen apparaît comme une machine abstraite, c’est-à-dire une instance capable d’auto-générer, d’auto-poïétiser, son propre mystère représentationnel.

Clique Irreducibility of Some Iterative Classes of Graphs

In this paper, two notions, the clique irreducibility and clique vertex irreducibility are discussed. A graph G is clique irreducible if every clique in G of size at least two, has an edge which does not lie in any other clique of G and it is clique vertex irreducible if every clique in G has a vertex which does not lie in any other clique of G. It is proved that L(G) is clique irreducible if and only if every triangle in G has a vertex of degree two. The conditions for the iterations of line graph, the Gallai graphs, the anti-Gallai graphs and its iterations to be clique irreducible and clique vertex irreducible are also obtained.

Phase modulation of microwave signals using point contact Germanium signal diodes

A forward - biased point contact germanium signal diode placed inside a waveguide section along the E -vector is found to introduce significant phase shift of microwave signals . The usefulness of the arrangement as a phase modulator for microwave carriers is demonstrated. While there is a less significant amplitude modulation accompanying phase modulation , the insertion losses are found to be negligible. The observations can be explained on the basis of the capacitance variation of the barrier layer with forward current in the diode

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Compact dual-band antenna for wireless access point

A compact dual-band printed antenna covering the 2.4 GHz (2400-2485 MHz) and 5.2 GHz (5150-5350 MHz) WLAN bands is presented. The experimental analysis shows a 2:1 VSWR bandwidth of up to 32 and 8% for 2.4 and 5.2 GHz, respectively. The measured radiation patterns are nearly omnidirectional, with moderate gain in both the WLAN bands.

Design and synthesis of efficient mac architectures for high speed decimal processor

Most of the commercial and financial data are stored in decimal fonn. Recently, support for decimal arithmetic has received increased attention due to the growing importance in financial analysis, banking, tax calculation, currency conversion, insurance, telephone billing and accounting. Performing decimal arithmetic with systems that do not support decimal computations may give a result with representation error, conversion error, and/or rounding error. In this world of precision, such errors are no more tolerable. The errors can be eliminated and better accuracy can be achieved if decimal computations are done using Decimal Floating Point (DFP) units. But the floating-point arithmetic units in today's general-purpose microprocessors are based on the binary number system, and the decimal computations are done using binary arithmetic. Only few common decimal numbers can be exactly represented in Binary Floating Point (BF P). ln many; cases, the law requires that results generated from financial calculations performed on a computer should exactly match with manual calculations. Currently many applications involving fractional decimal data perform decimal computations either in software or with a combination of software and hardware. The performance can be dramatically improved by complete hardware DFP units and this leads to the design of processors that include DF P hardware.VLSI implementations using same modular building blocks can decrease system design and manufacturing cost. A multiplexer realization is a natural choice from the viewpoint of cost and speed.This thesis focuses on the design and synthesis of efficient decimal MAC (Multiply ACeumulate) architecture for high speed decimal processors based on IEEE Standard for Floating-point Arithmetic (IEEE 754-2008). The research goal is to design and synthesize deeimal'MAC architectures to achieve higher performance.Efficient design methods and architectures are developed for a high performance DFP MAC unit as part of this research.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Approximate Approximations and a Boundary Point Method for the Linearized Stokes System

The method of approximate approximations, introduced by Maz'ya [1], can also be used for the numerical solution of boundary integral equations. In this case, the matrix of the resulting algebraic system to compute an approximate source density depends only on the position of a finite number of boundary points and on the direction of the normal vector in these points (Boundary Point Method). We investigate this approach for the Stokes problem in the whole space and for the Stokes boundary value problem in a bounded convex domain G subset R^2, where the second part consists of three steps: In a first step the unknown potential density is replaced by a linear combination of exponentially decreasing basis functions concentrated near the boundary points. In a second step, integration over the boundary partial G is replaced by integration over the tangents at the boundary points such that even analytical expressions for the potential approximations can be obtained. In a third step, finally, the linear algebraic system is solved to determine an approximate density function and the resulting solution of the Stokes boundary value problem. Even not convergent the method leads to an efficient approximation of the form O(h^2) + epsilon, where epsilon can be chosen arbitrarily small.

Computation of relativistic symmetry orbitals for finite double point groups

A program is presented for the construction of relativistic symmetry-adapted molecular basis functions. It is applicable to 36 finite double point groups. The algorithm, based on the projection operator method, automatically generates linearly independent basis sets. Time reversal invariance is included in the program, leading to additional selection rules in the non-relativistic limit.

Design and Control of an Anthropomorphic Robotic Finger with Multi-point Tactile Sensation

The goal of this research is to develop the prototype of a tactile sensing platform for anthropomorphic manipulation research. We investigate this problem through the fabrication and simple control of a planar 2-DOF robotic finger inspired by anatomic consistency, self-containment, and adaptability. The robot is equipped with a tactile sensor array based on optical transducer technology whereby localized changes in light intensity within an illuminated foam substrate correspond to the distribution and magnitude of forces applied to the sensor surface plane. The integration of tactile perception is a key component in realizing robotic systems which organically interact with the world. Such natural behavior is characterized by compliant performance that can initiate internal, and respond to external, force application in a dynamic environment. However, most of the current manipulators that support some form of haptic feedback either solely derive proprioceptive sensation or only limit tactile sensors to the mechanical fingertips. These constraints are due to the technological challenges involved in high resolution, multi-point tactile perception. In this work, however, we take the opposite approach, emphasizing the role of full-finger tactile feedback in the refinement of manual capabilities. To this end, we propose and implement a control framework for sensorimotor coordination analogous to infant-level grasping and fixturing reflexes. This thesis details the mechanisms used to achieve these sensory, actuation, and control objectives, along with the design philosophies and biological influences behind them. The results of behavioral experiments with a simple tactilely-modulated control scheme are also described. The hope is to integrate the modular finger into an %engineered analog of the human hand with a complete haptic system.

Feature Point Detection and Curve Approximation for Early Processing of Freehand Sketches

Freehand sketching is both a natural and crucial part of design, yet is unsupported by current design automation software. We are working to combine the flexibility and ease of use of paper and pencil with the processing power of a computer to produce a design environment that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in the sketch into the intended geometric objects using feature point detection and approximation. We demonstrate how multiple sources of information can be combined for feature detection in strokes and apply this technique using two approaches to signal processing, one using simple average based thresholding and a second using scale space.

On the Convergence of Stochastic Iterative Dynamic Programming Algorithms

Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(lambda) and Q-learning belong.

Application of discriminant function analysis and change-point problem in dating volcanic ashes

The application of Discriminant function analysis (DFA) is not a new idea in the study of tephrochrology. In this paper, DFA is applied to compositional datasets of two different types of tephras from Mountain Ruapehu in New Zealand and Mountain Rainier in USA. The canonical variables from the analysis are further investigated with a statistical methodology of change-point problems in order to gain a better understanding of the change in compositional pattern over time. Finally, a special case of segmented regression has been proposed to model both the time of change and the change in pattern. This model can be used to estimate the age for the unknown tephras using Bayesian statistical calibration