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18
Structured compressed sensing: From theory to applications
- IEEE TRANS. SIGNAL PROCESS
, 2011
"... Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard ..."
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Cited by 104 (16 self)
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Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.
Compressive Sensing Multi-User Detection with Block-Wise Orthogonal Least Squares
"... Abstract—One challenging future application in digital communications is the wireless uplink transmission in sensor networks. This application is characterized by sporadic transmissions by a large number of sensors over a random multiple access channel. To reduce control signaling overhead, we propo ..."
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Cited by 6 (5 self)
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Abstract—One challenging future application in digital communications is the wireless uplink transmission in sensor networks. This application is characterized by sporadic transmissions by a large number of sensors over a random multiple access channel. To reduce control signaling overhead, we propose that sensors do not transmit their activity states; instead sensor activity is detected at the receiver. As sensors have low activity probabilities, the multi-user vector is in general sparse. This enables Compressive Sensing (CS) detectors to perform joint Multi-User Detection (MUD) of activity and data, by exploiting the sparsity. Since sensors are either active or inactive for several symbol durations, block-wise CS detection can be applied to improve the activity detection. In this paper, we introduce blockwise greedy CS MUD, compare it to symbol-wise greedy CS MUD, and show that statistically independent channels for each symbol further improve the activity detection for block-wise CS detection. Herein, we use Code Division Multiple Access (CDMA) as a multiple access scheme. I.
Group Model Selection Using Marginal Correlations: The Good, the Bad and the Ugly
"... Abstract — Group model selection is the problem of determining a small subset of groups of predictors (e.g., the expression data of genes) that are responsible for majority of the variation in a response variable (e.g., the malignancy of a tumor). This paper focuses on group model selection in high- ..."
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Cited by 4 (1 self)
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Abstract — Group model selection is the problem of determining a small subset of groups of predictors (e.g., the expression data of genes) that are responsible for majority of the variation in a response variable (e.g., the malignancy of a tumor). This paper focuses on group model selection in high-dimensional linear models, in which the number of predictors far exceeds the number of samples of the response variable. Existing works on high-dimensional group model selection either require the number of samples of the response variable to be significantly larger than the total number of predictors contributing to the response or impose restrictive statistical priors on the predictors and/or nonzero regression coefficients. This paper provides comprehensive understanding of a low-complexity approach to group model selection that avoids some of these limitations. The proposed approach, termed Group Thresholding (GroTh), is based on thresholding of marginal correlations of groups of predictors with the response variable and is reminiscent of existing thresholding-based approaches in the literature. The most important contribution of the paper in this regard is relating the performance of GroTh to a polynomial-time verifiable property of the predictors for the general case of arbitrary (random or deterministic) predictors and arbitrary nonzero regression coefficients.
Internet Accessible
- Lanzhou University, Lanzhou
, 1998
"... DNA molecular weight standard control, also called DNA marker (ladder), has been widely used in the experiments of molecular biology. In the paper, we report a method by which DNA marker was prepared based on multiple PCR technique. 100-1000 bp DNA fragments were amplified using the primers designe ..."
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Cited by 2 (2 self)
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DNA molecular weight standard control, also called DNA marker (ladder), has been widely used in the experiments of molecular biology. In the paper, we report a method by which DNA marker was prepared based on multiple PCR technique. 100-1000 bp DNA fragments were amplified using the primers designed according to the 6631 ∼ 7630 position of lambda DNA. Target DNA fragments were amplified using Touchdown PCR combined with hot start PCR, respectively, followed extracted by phenol/chloroform, precipitated with ethanol and mixed thoroughly. The results showed that the 100-1000 bp DNA fragments were successfully obtained in one PCR reaction, the bands of prepared DNA marker were clear, the size was right and could be used as control in the molecular biology experiment. This method could save time and be more inexpensive, rapid, simple when compared with the current DNA Ladder prepared means.
A Novel Uplink Data Transmission Scheme For Small Packets
- In Massive MIMO System,” to appear at IEEE/CIC 2014 Symposium on Signal Processing for Communications (ICCC
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Digital-Assisted Asynchronous Compressive Sensing Front-End
"... Abstract—Compressive sensing (CS) is a promising technique that enables sub-Nyquist sampling, while still guaranteeing the re-liable signal recovery. However, existing mixed-signal CS front-end implementation schemes often suffer from high power con-sumption and nonlinearity. This paper presents a d ..."
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Cited by 1 (1 self)
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Abstract—Compressive sensing (CS) is a promising technique that enables sub-Nyquist sampling, while still guaranteeing the re-liable signal recovery. However, existing mixed-signal CS front-end implementation schemes often suffer from high power con-sumption and nonlinearity. This paper presents a digital-assisted asynchronous compressive sensing (DACS) front-end which offers lower power and higher reconstruction performance relative to the conventional CS-based approaches. The front-end architecture leverages a continuous-time ternary encoding scheme which mod-ulates amplitude variation to ternary timing information. Power is optimized by employing digital-assisted modules in the front-end circuit and a part-time operation strategy for high-power mod-ules. An-member Group-based Total Variation (-GTV) algo-rithm is proposed for the sparse reconstruction of piecewise-con-stant signals. By including both the inter-group and intra-group total variation, the-GTV scheme outperforms the conventional TV-based methods in terms of faster convergence rate and better sparse reconstruction performance. Analyses and simulations with a typical ECG recording system confirm that the proposed DACS front-end outperforms a conventional CS-based front-end using a randomdemodulator in terms of lower power consumption, higher recovery performance, and more system flexibility. Index Terms—Asynchronous architecture, compressive sensing (CS), continuous-time ternary encoding, digital-assisted front-end, part-time randomization, total variation. I.
Accuracy guaranties for ℓ1 recovery of block-sparse signals
, 2012
"... We discuss new methods for the recovery of signals with block-sparse structure, based on ℓ1-minimization. Our emphasis is on verifiable conditions on the problem parameters (sensing matrix and the block structure) for accurate recovery and efficiently computable bounds for the recovery error. These ..."
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We discuss new methods for the recovery of signals with block-sparse structure, based on ℓ1-minimization. Our emphasis is on verifiable conditions on the problem parameters (sensing matrix and the block structure) for accurate recovery and efficiently computable bounds for the recovery error. These bounds are then optimized with respect to the method parameters to construct the estimators with improved statistical properties. To justify the proposed approach we provide an oracle inequality, which links the properties of the recovery algorithms and the best estimation performance. We also propose a new matching pursuit algorithm for block-sparse recovery.
