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POSTER SOCHIAS 2017: Blazars study with the 40m telescope
Blazars are a type of Active Galactic Nucleus (AGN), characterized by the emission of a relativistic jet that points close to our line of sight. They are extremely powerful, variable emitters from radio to gamma-ray wavelengths. By cross-correlating light-curves from different energies, it is possible to determine the physical condition of the blazar emitting region, i.e. mechanism, morphology, distance from block hole to the actual emission, etc. In 2007, the 40m telescope at the Owens Valley Radio Observatory (OVRO) embarked on a new research campaign. In support of the Fermi Gamma-ray Space Telescope, the OVRO 40m telescope is monitoring more than 1800 blazars twice per week. In 2014, a new Ku-band spectropolarimeter receiver, KuPol, was installed on the 40m telescope with the aim of elucidating about potential spectral fluctuations that may arise during the flaring events. In this poster, we will present relevant information and preliminar results from KuPol and the actual status of its calibration, and also as well as the current state of the PSD analysis, where we are applying Fourier Transform to the signals.
The Sum of the Reciprocals of the Squares
A reproduction of a proof by Leonard Euler, which I dug out of the back of a calculus textbook with the help of Brian Burrell. This was in MATH 233 at the University of Massachusetts, Amherst, Fall 2015.
Applications of Compressive Sensing in Communications and Signal Processing
Compressive Sensing is a Signal Processing technique, which gave a break through in 2004. The main idea of CS is, by exploiting the sparsity nature of the signal (in any domain), we can reconstruct the signal from very fewer samples than required by Shannon-Nyquist sampling theorem. Reconstructing a sparse signal from fewer samples is equivalent to solving a under-determined system with sparsity constraints. Least square solution to such a problem yield poor `results because sparse signals cannot be well approximated to a least norm solution. Instead we use l1 norm(convex) to solve this problem which is the best approximation to the exact solution given by l0 norm(non-convex). In this paper we plan to discuss three applications of CS in estimation theory. They are, CS based reliable Channel estimation assuming sparsity in the channel is known for TDS-OFDM systems. Indoor location estimation from received signal strength (RSS) where CS is used to reconstruct the radio map from RSS measurements. Identifying that subspace in which the signal of interest lies using ML estimation, assuming signal lies in a union of subspaces which is a standard sparsity assumption according to CS theory. Index terms : Compressive Sensing, Indoor positioning, fingerprinting, radio map, Maximum likelihood estimation, union of linear subspaces, subspace recovery.
Este ejemplo representa el esquema de un amplificador diferencial construido con un amplificador operacional y cinco resistencias, el cual se usa para calcular la ganancia de la diferencia de dos señales independientes. Las notaciones son las siguientes:
v1: tensión de entrada 1.
v2: tensión de entrada 2.
RL: resistencia de carga.
vo: tensión de salida.
Este esquema es una adaptación del que se encuentra en el la página 76, Capítulo 1 del texto "Electrónica, 2da Edición" de Allan R. Hambley, publicado en idioma español por la editorial Pearson Educación.