58,383 research outputs found
Recursive Compressed Sensing
We introduce a recursive algorithm for performing compressed sensing on
streaming data. The approach consists of a) recursive encoding, where we sample
the input stream via overlapping windowing and make use of the previous
measurement in obtaining the next one, and b) recursive decoding, where the
signal estimate from the previous window is utilized in order to achieve faster
convergence in an iterative optimization scheme applied to decode the new one.
To remove estimation bias, a two-step estimation procedure is proposed
comprising support set detection and signal amplitude estimation. Estimation
accuracy is enhanced by a non-linear voting method and averaging estimates over
multiple windows. We analyze the computational complexity and estimation error,
and show that the normalized error variance asymptotically goes to zero for
sublinear sparsity. Our simulation results show speed up of an order of
magnitude over traditional CS, while obtaining significantly lower
reconstruction error under mild conditions on the signal magnitudes and the
noise level.Comment: Submitted to IEEE Transactions on Information Theor
Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms
Algorithms for Massive MIMO uplink detection and downlink precoding typically
rely on a centralized approach, by which baseband data from all antenna modules
are routed to a central node in order to be processed. In the case of Massive
MIMO, where hundreds or thousands of antennas are expected in the base-station,
said routing becomes a bottleneck since interconnection throughput is limited.
This paper presents a fully decentralized architecture and an algorithm for
Massive MIMO uplink detection and downlink precoding based on the Stochastic
Gradient Descent (SGD) method, which does not require a central node for these
tasks. Through a recursive approach and very low complexity operations, the
proposed algorithm provides a good trade-off between performance,
interconnection throughput and latency. Further, our proposed solution achieves
significantly lower interconnection data-rate than other architectures,
enabling future scalability.Comment: Manuscript accepted for publication in IEEE Transactions on Signal
Processin
Sensor-Based Estimation of BTEX Concentrations in Water Samples Using Recursive Least Squares and Kalman Filter Techniques
This work investigates sensor signal processing approaches that can be used with a sensor system for direct on-site monitoring of groundwater, enabling detection and quantification of BTEX (benzene, toluene, ethylbenzene and xylene) compounds at μg/L (ppb) concentrations in the presence of interferents commonly found in groundwater. A model for the sensor response to water samples containing multiple analytes was first formulated based on experimental results. The first signal processing approach utilizes only RLSE (recursive least squares estimation) whereas the second, a two-step processing technique, utilizes both RLSE and bank of Kalman filters for the estimation process. The estimation techniques were tested using actual sensor responses to contaminated groundwater samples. Results indicate that relatively accurate concentration estimates (within ±15–23% for benzene) can be obtained in near-real time using these techniques. The two-step processing technique gave more accurate results. This approach allows the use of a single sensor, even for multiple analyte detection and quantification
A New Approach to Extract Fetal Electrocardiogram Using Affine Combination of Adaptive Filters
The detection of abnormal fetal heartbeats during pregnancy is important for
monitoring the health conditions of the fetus. While adult ECG has made several
advances in modern medicine, noninvasive fetal electrocardiography (FECG)
remains a great challenge. In this paper, we introduce a new method based on
affine combinations of adaptive filters to extract FECG signals. The affine
combination of multiple filters is able to precisely fit the reference signal,
and thus obtain more accurate FECGs. We proposed a method to combine the Least
Mean Square (LMS) and Recursive Least Squares (RLS) filters. Our approach found
that the Combined Recursive Least Squares (CRLS) filter achieves the best
performance among all proposed combinations. In addition, we found that CRLS is
more advantageous in extracting FECG from abdominal electrocardiograms (AECG)
with a small signal-to-noise ratio (SNR). Compared with the state-of-the-art
MSF-ANC method, CRLS shows improved performance. The sensitivity, accuracy and
F1 score are improved by 3.58%, 2.39% and 1.36%, respectively.Comment: 5 pages, 4 figures, 3 table
Estimation of pulse heights and arrival times
The problem is studied of estimating the arrival times and heights of pulses of known shape observed with white additive noise. The main difficulty is estimating the number of pulses. When a maximum likelihood formulation is employed for the estimation problem, difficulties similar to the problem of estimating the order of an unknown system arise. The problem may be overcome using Rissanen's shortest data description approach. An estimation algorithm is described, and its consistency is proved. The results are illustrated by a simulation study using an example from seismic data processing also studied by Mendel
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