16,975 research outputs found
Introduction to Random Signals and Noise
Random signals and noise are present in many engineering systems and networks. Signal processing techniques allow engineers to distinguish between useful signals in audio, video or communication equipment, and interference, which disturbs the desired signal. With a strong mathematical grounding, this text provides a clear introduction to the fundamentals of stochastic processes and their practical applications to random signals and noise. With worked examples, problems, and detailed appendices, Introduction to Random Signals and Noise gives the reader the knowledge to design optimum systems for effectively coping with unwanted signals.\ud
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Key features:\ud
âą Considers a wide range of signals and noise, including analogue, discrete-time and bandpass signals in both time and frequency domains.\ud
âą Analyses the basics of digital signal detection using matched filtering, signal space representation and correlation receiver.\ud
âą Examines optimal filtering methods and their consequences.\ud
âą Presents a detailed discussion of the topic of Poisson processed and shot noise.\u
Tracking Cell Signals in Fluorescent Images
In this paper we present the techniques for tracking cell signal in GFP (Green Fluorescent Protein) images of growing cell colonies. We use such tracking for both data extraction and dynamic modeling of intracellular processes. The techniques are based on optimization of energy functions, which simultaneously determines cell correspondences, while estimating the mapping functions. In addition to spatial mappings such as affine and Thin-Plate Spline mapping, the cell growth and cell division histories must be estimated as well. Different levels of joint optimization are discussed. The most unusual tracking feature addressed in this paper is the possibility of one-to-two correspondences caused by cell division. A novel extended softassign algorithm for solutions of one-to-many correspondences is detailed in this paper. The techniques are demonstrated on three sets of data: growing bacillus Subtillus and e-coli colonies and a developing plant shoot apical meristem. The techniques are currently used by biologists for data extraction and hypothesis formation
An improved adaptive sidelobe blanker
We propose a two-stage detector consisting of a subspace
detector followed by the whitened adaptive beamformer orthogonal rejection test. The performance analysis shows that it possesses the constant false alarm rate property with respect to the unknown covariance matrix of the noise and that it can guarantee a wider range of directivity values with respect to previously proposed two-stage detectors. The probability of false alarm and the probability of detection (for both matched and mismatched signals) have been evaluated by means of numerical integration techniques
Neural Nearest Neighbors Networks
Non-local methods exploiting the self-similarity of natural signals have been
well studied, for example in image analysis and restoration. Existing
approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed
feature space. The main hurdle in optimizing this feature space w.r.t.
application performance is the non-differentiability of the KNN selection rule.
To overcome this, we propose a continuous deterministic relaxation of KNN
selection that maintains differentiability w.r.t. pairwise distances, but
retains the original KNN as the limit of a temperature parameter approaching
zero. To exploit our relaxation, we propose the neural nearest neighbors block
(N3 block), a novel non-local processing layer that leverages the principle of
self-similarity and can be used as building block in modern neural network
architectures. We show its effectiveness for the set reasoning task of
correspondence classification as well as for image restoration, including image
denoising and single image super-resolution, where we outperform strong
convolutional neural network (CNN) baselines and recent non-local models that
rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at
https://github.com/visinf/n3net
Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory
Mental simulation is a critical cognitive function for goal-directed behavior
because it is essential for assessing actions and their consequences. When a
self-generated or externally specified goal is given, a sequence of actions
that is most likely to attain that goal is selected among other candidates via
mental simulation. Therefore, better mental simulation leads to better
goal-directed action planning. However, developing a mental simulation model is
challenging because it requires knowledge of self and the environment. The
current paper studies how adequate goal-directed action plans of robots can be
mentally generated by dynamically organizing top-down visual attention and
visual working memory. For this purpose, we propose a neural network model
based on variational Bayes predictive coding, where goal-directed action
planning is formulated by Bayesian inference of latent intentional space. Our
experimental results showed that cognitively meaningful competencies, such as
autonomous top-down attention to the robot end effector (its hand) as well as
dynamic organization of occlusion-free visual working memory, emerged.
Furthermore, our analysis of comparative experiments indicated that
introduction of visual working memory and the inference mechanism using
variational Bayes predictive coding significantly improve the performance in
planning adequate goal-directed actions
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