16 research outputs found

    Improved Feature Extraction, Feature Selection, and Identification Techniques That Create a Fast Unsupervised Hyperspectral Target Detection Algorithm

    Get PDF
    This research extends the emerging field of hyperspectral image (HSI) target detectors that assume a global linear mixture model (LMM) of HSI and employ independent component analysis (ICA) to unmix HSI images. Via new techniques to fully automate feature extraction, feature selection, and target pixel identification, an autonomous global anomaly detector, AutoGAD, has been developed for potential employment in an operational environment for real-time processing of HSI targets. For dimensionality reduction (initial feature extraction prior to ICA), a geometric solution that effectively approximates the number of distinct spectral signals is presented. The solution is based on the theory of the shape of the eigenvalue curve of the covariance matrix of spectral data containing noise. For feature selection, previously a subjective definition called significant kurtosis change was used to denote the separation between targets classes and non-target classes. This research presents two new measures, potential target signal to noise ratio (PT SNR) and max pixel score which computed for each of the ICA features to create a new two dimensional feature space where the overlap between target and non-target classes is reduced compared to the one dimensional kurtosis value feature space. Finally, after target feature selection, adaptive noise filtering, but with an iterative approach, is applied to the signals. The effect is a reduction in the power of the noise while preserving the power of the target signal prior to target identification to reduce false positive detections. A zero-detection histogram method is applied to the smoothed signals to identify target locations to the user. MATLAB code for the AutoGAD algorithm is provided

    Réduction d'interférence dans les systèmes de transmission sans fil

    Get PDF
    Wireless communications have known an exponential growth and a fast progress over the past few decades. Nowadays, wireless mobile communications have evolved over time starting with the first generation primarily developed for voice communications, and reaching the fourth generation referred to as long term evolution (LTE) that offers an increasing capacity and speed using a different radio interface together with core network improvements. Overall throughput and transmission reliability are among the essential measures of service quality in a wireless system. Such measures are mainly subjected to interference management constraint in a multi-user network. The interference management is at the heart of wireless regulation and is essential for maintaining a desirable throughput while avoiding the detrimental impact of interference at the undesired receivers. Our work is incorporated within the framework of interference network where each user is equipped with single or multiple antennas. The goal is to resolve the challenges that the communications face taking into account the achievable rate and the complexity cost. We propose several solutions for the precoding and decoding designs when transmitters have limited cooperation based on a technique called Interference Alignment. We also address the detection scheme in the absence of any precoding design and we introduce a low complexity detection scheme based on the sparse decomposition.Les communications mobiles sans fil ont connu un formidable essor au cours des dernières décennies. Tout a commencé avec les services vocaux offerts par les systèmes de la première génération en 1980, jusqu¿aux systèmes de la quatrième génération aujourd¿hui avec des services internet haut débit et un accroissement du nombre d¿utilisateurs. En effet, les caractéristiques essentielles qui définissent les services et la qualité de ces services dans les systèmes de communication sans fil sont: le débit, la fiabilité de transmission et le nombre d¿utilisateurs. Ces caractéristiques sont fortement liées entre elles et sont dépendantes de la gestion des interférences entre les différents utilisateurs. Les interférences entre-utilisateurs se produisent lorsque plusieurs émetteurs, dans une même zone, transmettent simultanément en utilisant la même bande de fréquence. Dans cette thèse, nous nous intéressons à la gestion d¿interférence entre utilisateurs par le biais de l¿approche d¿alignement d¿interférences où la coopération entre utilisateurs est réduite. Aussi, nous nous sommes intéressés au design d¿un récepteur où l¿alignement d¿interférences n¿est pas utilisé et où la gestion des interférences est réalisée par des techniques de décodage basées sur les décompositions parcimonieuses des signaux de communications. Ces approches ont conduit à des méthodes performantes et peu couteuses, exploitables dans les liens montant ou descendant

    Development of Novel Independent Component Analysis Techniques and their Applications

    Get PDF
    Real world problems very often provide minimum information regarding their causes. This is mainly due to the system complexities and noninvasive techniques employed by scientists and engineers to study such systems. Signal and image processing techniques used for analyzing such systems essentially tend to be blind. Earlier, training signal based techniques were used extensively for such analyses. But many times either these training signals are not practicable to be availed by the analyzer or become burden on the system itself. Hence blind signal/image processing techniques are becoming predominant in modern real time systems. In fact, blind signal processing has become a very important topic of research and development in many areas, especially biomedical engineering, medical imaging, speech enhancement, remote sensing, communication systems, exploration seismology, geophysics, econometrics, data mining, sensor networks etc. Blind Signal Processing has three major areas: Blind Signal Separation and Extraction, Independent Component Analysis (ICA) and Multichannel Blind Deconvolution and Equalization. ICA technique has also been typically applied to the other two areas mentioned above. Hence ICA research with its wide range of applications is quite interesting and has been taken up as the central domain of the present work

    PSA 2016

    Get PDF
    These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2016

    Einstein vs. Bergson

    Get PDF
    On 6 April 1922, Einstein met Bergson to debate the nature of time: is the time the physicist calculates the same time the philosopher reflects on? Einstein claimed that only scientific time is real, while Bergson argued that scientific time always presupposes a living and perceiving subject. On that day, nearly 100 years ago, conflict was inevitable. Is it still inevitable today? How many kinds of time are there

    A Defense of Pure Connectionism

    Full text link
    Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming increasingly clear, in practice) the resources to model even the most rich and distinctly human cognitive capacities, such as abstract, conceptual thought and natural language comprehension and production. Consonant with much previous philosophical work on connectionism, I argue that a core principle—that proximal representations in a vector space have similar semantic values—is the key to a successful connectionist account of the systematicity and productivity of thought, language, and other core cognitive phenomena. My work here differs from preceding work in philosophy in several respects: (1) I compare a wide variety of connectionist responses to the systematicity challenge and isolate two main strands that are both historically important and reflected in ongoing work today: (a) vector symbolic architectures and (b) (compositional) vector space semantic models; (2) I consider very recent applications of these approaches, including their deployment on large-scale machine learning tasks such as machine translation; (3) I argue, again on the basis mostly of recent developments, for a continuity in representation and processing across natural language, image processing and other domains; (4) I explicitly link broad, abstract features of connectionist representation to recent proposals in cognitive science similar in spirit, such as hierarchical Bayesian and free energy minimization approaches, and offer a single rebuttal of criticisms of these related paradigms; (5) I critique recent alternative proposals that argue for a hybrid Classical (i.e. serial symbolic)/statistical model of mind; (6) I argue that defending the most plausible form of a connectionist cognitive architecture requires rethinking certain distinctions that have figured prominently in the history of the philosophy of mind and language, such as that between word- and phrase-level semantic content, and between inference and association
    corecore