The role of filter banks in sinusoidal frequency estimation

2001 ◽  
Vol 338 (5) ◽  
pp. 517-547 ◽  
Author(s):  
Andre Tkacenko ◽  
P.P. Vaidyanathan
Keyword(s):  
Filter Banks ◽  
Frequency Estimation

Efficient Multirate Filtering

2011 ◽  
pp. 1294-1299
Author(s):  
Ljiljana D. Milic
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Filter ◽  
Input Data ◽  
Filter Banks ◽  
Sampling Rate ◽  
Digital Audio ◽  
Multirate Filter Banks ◽  
Computation Speed

A multirate filter can be defined as a digital filter in which the input data rate is changed in one or more intermediate points. With the efficient multirate approach, computations are evaluated at the lowest possible sampling rate, thus improving the computational efficiency, increasing the computation speed, and lowering the power consumption. Multirate filters are of essential importance for communications, image processing, digital audio, and multimedia. The role of multirate filtering in modern signal processing systems is threefold: Firstly, they are used whenever there is a need to preserve the signal properties when connecting two systems operating at different sampling rates. Secondly, multirate techniques are used for constructing filters with stringent spectral constraints that are very difficult, even impossible, to be solved otherwise. Thirdly, multirate filters are used in constructing multirate filter banks.

scholarly journals The U2 snDNA Is a Useful Marker for B Chromosome Detection and Frequency Estimation in the Grasshopper Abracris flavolineata

2017 ◽  
Vol 151 (1) ◽  
pp. 36-40 ◽  
Author(s):  
Diogo Milani ◽  
Octavio M. Palacios-Gimenez ◽  
Diogo C. Cabral-de-Mello
Keyword(s):  
Evolutionary History ◽  
Frequency Estimation ◽  
B Chromosome ◽  
B Chromosomes ◽  
Future Studies ◽  
Functional Studies ◽  
Chromosome Transmission ◽  
Somatic Tissues ◽  
Frequency Changes

In this study, we describe a strategy to determine the presence of B chromosomes in the living grasshopper Abracris flavolineata by FISH using U2 snDNA as a probe in interphase hemolymph nuclei. In individuals without B chromosomes, (0B) 2 dot signals were noticed, corresponding to A complement U2 snDNA clusters. In +1B and +2B individuals, 4 or 8 additional signals were noticed, respectively. In all cases, the absence or presence of 1 or 2 B chromosomes correlated in hemolymph and in somatic or germline tissues, validating the efficiency of the marker. Our data suggest that the B chromosome of A. flavolineata is present in all somatic tissues. B-carrying individuals showed the same number of B chromosomes in germ and somatic cells, suggesting that the B is mitotically stable. The marker was used to compare B chromosome frequency in the analyzed population with a sample collected previously, in order to test for B frequency changes and differences of B chromosome prevalence among sexes, but no statistically significant differences were noticed. The identification of living animals harboring B chromosomes will be very useful in future studies of B chromosome transmission, as well as in functional studies involving RNA analysis, thus contributing to the understanding of evolutionary history and the possible role of the B chromosome in A. flavolineata.

scholarly journals Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2729
Author(s):  
Hind R. Almayyali ◽  
Zahir M. Hussain
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
Frequency Estimation ◽  
Spectral Techniques ◽  
Sensor Applications ◽  
Resource Limited ◽  
Estimate Frequency ◽  
Reduced Complexity ◽  
Efficient Software

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.

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