324 research outputs found
The Cognitive Compressive Sensing Problem
In the Cognitive Compressive Sensing (CCS) problem, a Cognitive Receiver (CR)
seeks to optimize the reward obtained by sensing an underlying dimensional
random vector, by collecting at most arbitrary projections of it. The
components of the latent vector represent sub-channels states, that change
dynamically from "busy" to "idle" and vice versa, as a Markov chain that is
biased towards producing sparse vectors. To identify the optimal strategy we
formulate the Multi-Armed Bandit Compressive Sensing (MAB-CS) problem,
generalizing the popular Cognitive Spectrum Sensing model, in which the CR can
sense out of the sub-channels, as well as the typical static setting of
Compressive Sensing, in which the CR observes linear combinations of the
dimensional sparse vector. The CR opportunistic choice of the sensing
matrix should balance the desire of revealing the state of as many dimensions
of the latent vector as possible, while not exceeding the limits beyond which
the vector support is no longer uniquely identifiable.Comment: 8 pages, 2 figure
Memristor-based Circuits for Performing Basic Arithmetic Operations
In almost all of the currently working circuits, especially in analog
circuits implementing signal processing applications, basic arithmetic
operations such as multiplication, addition, subtraction and division are
performed on values which are represented by voltages or currents. However, in
this paper, we propose a new and simple method for performing analog arithmetic
operations which in this scheme, signals are represented and stored through a
memristance of the newly found circuit element, i.e. memristor, instead of
voltage or current. Some of these operators such as divider and multiplier are
much simpler and faster than their equivalent voltage-based circuits and they
require less chip area. In addition, a new circuit is designed for programming
the memristance of the memristor with predetermined analog value. Presented
simulation results demonstrate the effectiveness and the accuracy of the
proposed circuits.Comment: 5pages, 4 figures, Accepted in World Conference on Information
Technology, turkey, 201
Memristor Crossbar-based Hardware Implementation of IDS Method
Ink Drop Spread (IDS) is the engine of Active Learning Method (ALM), which is
the methodology of soft computing. IDS, as a pattern-based processing unit,
extracts useful information from a system subjected to modeling. In spite of
its excellent potential in solving problems such as classification and modeling
compared to other soft computing tools, finding its simple and fast hardware
implementation is still a challenge. This paper describes a new hardware
implementation of IDS method based on the memristor crossbar structure. In
addition of simplicity, being completely real-time, having low latency and the
ability to continue working after the occurrence of power breakdown are some of
the advantages of our proposed circuit.Comment: 16 pages, 13 figures, Submitted to IEEE Transaction on Fuzzy System
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