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{"id":28714,"date":"2012-11-05T13:21:00","date_gmt":"2012-11-05T04:21:00","guid":{"rendered":"https:\/\/heungno.net\/?p=28714"},"modified":"2024-01-02T14:44:46","modified_gmt":"2024-01-02T05:44:46","slug":"information-and-system-group","status":"publish","type":"post","link":"https:\/\/heungno.net\/?p=28714","title":{"rendered":"Information and System Group"},"content":{"rendered":"

Information and System Group seminar materials<\/p>\n\n\n

November 2015<\/p>\n\n\n\n

1<\/td><\/td>2015-11-27<\/td>Support recovery with orthogonal matching pursuit in the presence of noise<\/td>Jang Jehyuk<\/td>PDF<\/a><\/td><\/td><\/tr>
<\/td><\/td><\/td>In this paper, sufficient and necessary conditions for correct recovery of sparse solution using OMP under the noisy environment are provided. In addition, upper bound of the error rate in recovering is studied. The results are characterized by restricted isometry constant and signal-to-noise ratio.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

July 2015<\/p>\n\n\n\n

1<\/td><\/td>2015-07-20<\/td>An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions<\/td>Jusung Kang<\/td>(PDF)<\/span><\/u><\/a><\/td><\/td><\/tr>
<\/td><\/td><\/td>In this paper, the effects of environmental conditions on a radio-transmitter identification system are investigated<\/strong><\/strong>. C<\/u><\/em>hanging in environmental conditions causes the feature vectors to spread over the feature space,<\/u><\/em> resulting in classification performance degradation. This performance degradation can be compensated for by training the system with data collected in a range of battery-voltage and ambient temperature levels.<\/u><\/em> The SNR vs Classification accuracy is also determined for systems operating in noisy channels. The transient features are extracted using a technique based on the amplitudes and phase changes of the received signals<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

June 2015<\/p>\n\n\n\n

1<\/td><\/td>2015-06-29<\/td>Coexistence Decision Making for Spectrum Sharing among Heterogeneous Wireless Systems<\/td>Muhammad asif<\/td>(PDF)<\/a><\/td><\/td><\/tr>
<\/td><\/td><\/td>In this paper authors tackle spectrum sharing with an objective of enabling coexistence among dissimilar TVWS networks. The sharing problem is defined as multi-objective optimization problem (MOOP). An algorithm to solve the MOOP has also been presented in the paper. Finally the simulation study shows the superiority of the proposed algorithm over existing coexistence decision making algorithms in terms of fairness and percentage of demand served.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

April 2015<\/p>\n\n\n\n

1<\/td><\/td>2015-04-13<\/td>Fast Non-Negative Orthogonal Matching Pursuit<\/td>Haeung Choi<\/td> (PDF)<\/span><\/span><\/a><\/td><\/td><\/tr>
<\/td><\/td><\/td>Non-negative signal is an important class of sparse signals. Many algorithms have already been proposed to recover such non-negative sparse representations. One of such modification is Non-Negative Orthogonal Matching Pursuit(NNOMP), which satisfies non-negative constraint by selecting positive coefficients and using a non-negative optimization technique. However, since it consumes the extra computational costs, the most significant benefit of OMP\u2014fast implementation\u2014cannot be exhibited. Therefore, author proposed a fast implementation of NNOMP similar with canonical fast OMP based on QR decomposition method, called Fast Non-Negative Orthogonal Matching Pursuit(FNNOMP).<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
2<\/td><\/td>2015-04-27<\/td>Opportunistic Routing Algorithm for Relay Node Selection in Wireless Sensor Networks<\/td>Zafar Iqbal<\/td>(DOC)<\/a><\/td><\/td><\/tr>
<\/td><\/td><\/td>Energy saving optimization has become one of the major concerns in wireless sensor network (WSN) routing protocol design, due to the fact that most sensor nodes are equipped with limited non-rechargeable battery power. This paper focuses on minimizing energy consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue network. Using opportunistic routing theory, multihop relay decision to optimize the network energy efficiency is made based on the differences among sensor nodes, in terms of both their distance to sink and the residual energy of each other. Specifically, an Energy Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power cost during data relay and protect the nodes with relatively low residual energy. Extensive simulations and real testbed results show that the proposed solution ENS_OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n


\nMarch 2015<\/p>\n\n\n\n\n\n\n\n

1<\/td><\/td>2015-03-23<\/td> Cooperative Spectrum Access Towards Secure
\nInformation Transfer for CRNs<\/td>		Asif<\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td>Encryption at upper layers becomes challenging for a network without infrastructure. Moreover, the encryption algorithms could be compromised. An alternative way for enhancing the security is to use physical layer security, or information-theoretic security by protecting the transmitted signal from being received or decoded by the eavesdroppers. In this paper authors make use of it to introduce a cooperative spectrum access for secure information transfer in CRNs. They show that security of the PU\u2019s information is enhanced using SUs meanwhile SUs in reward get fair opportunity to access channel for their own information transfer.<\/td><\/tr>
2<\/td><\/td>2015-03-30<\/td>Specific emitter identification based on Hilbert-Huang transform-based time-frequency-energy distribution features<\/td>Jusung Kang<\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td>In this paper, a novel specific emitter identification method is proposed. The time-frequency-energy distribution of transient signal is obtained by Hilbert-Huang transform (HHT). The phase based method and self-adaptive threshold based on the HHT is used to extract the transient signal. After that, PCA and SVM is used for classification. 8 mobile phones are used to evaluate the performance of this proposed method.The result said that this proposed method is more useful than other RF fingerprint method based on amplitude, phase, frequency, and energy envelop. Also they said that this method could be applicable for any security issues of wireless network.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n


\nJanuary 2015<\/p>\n\n\n\n\n\n\n\n

1<\/td><\/td>2015-01-12<\/td>Sparsity-Aware Sphere Decoding: Algorithms and Complexity Analysis<\/td>Hwanchol Jang<\/td>(doc)<\/td><\/td><\/tr>
<\/td><\/td><\/td>Integer least-squares problems, concerned with solving a system of equations where the components of the unknown vector are integer-valued, arise in a wide range of applications. In many scenarios the unknown vector is sparse, i.e., a large fraction of its entries are zero. Examples include applications in wireless communications, digital fingerprinting, and array-comparative genomic hybridization systems. Sphere decoding, commonly used for solving integer least-squares problems, can utilize the knowledge about sparsity of the unknown vector to perform computationally efficient search for the solution. In this paper, we formulate and analyze the sparsity-aware sphere decoding algorithm that imposes L0-norm constraint on the admissible solution. Analytical expressions for the expected complexity of the algorithm for alphabets typical of sparse channel estimation and source allocation applications are derived and validated through extensive simulations. The results demonstrate superior performance and speed of sparsity-aware sphere decoder compared to the conventional sparsity-unaware sphere decoding algorithm. Moreover, variance of the complexity of the sparsity-aware sphere decoding algorithm for binary alphabets is derived. The search space of the proposed algorithm can be further reduced by imposing lower bounds on the value of the objective function. The algorithm is modified to allow for such a lower bounding technique and simulations illustrating efficacy of the method are presented. Performance of the algorithm is demonstrated in an application to sparse channel estimation, where it is shown that sparsity-aware sphere decoder performs close to theoretical lower limits.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

December 2014<\/p>\n\n\n\n

1<\/td> 2014-12-08<\/td>K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation<\/td><\/td>Haeung<\/td><\/td><\/td><\/tr>
<\/td><\/td> Using an overcomplete dictionary that contains prototype of signal-atoms, signals are described by sparse linear combinations of these atoms. In this paper, authors introduce a novel algorithm for adapting dictionaries in order to achieve sparse representations\u2014the k-SVD algorithm. This algorithm repeats sparse coding with current dictionary and updating the dictionary to better fit the data. And the authors analyze this algorithm and demonstrate its results.<\/td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
2<\/td> 2014-12-22<\/td>Fronthaul Compression for Cloud Radio Access Networks<\/td><\/td>Zafar<\/td><\/td> (doc)<\/a><\/td><\/tr>
<\/td><\/td>Cloud Radio Access Networks (C-RANs) are novel next-generation wireless cellular network architectures where the baseband processing is shifted from the base station (BS) to the control unit (CU) in the \u201ccloud\u201d of the operator. In C-RANs, unlike the current cellular systems, the functionalities needed to process the IQ samples of the radio signals received\/transmitted by the RUs are not implemented in the RUs but in the CU within the cloud of the core network. The BSs which function as radio units (RUs) are connected to the CU via fronthaul links. The fronthaul links carry information to the CU from the RU and vice versa in the form of quantized in-phase and quadrature (IQ) samples. However, the current solutions for standardization efforts use conventional scalar quantization techniques which impose a bottleneck to the system performance due to limitations in the fronthaul capacity. Due the large amount of IQ data generated by the network and limited capacity of the fronthaul links, compression prior to transmission on the fronthaul is very necessary and is of significant importance in the design of next generation wireless cellular networks. This article provides a summary of the recent work in this area with emphasis on the advanced signal processing solutions.<\/td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
3<\/td> 2014-12-29<\/td>Information-theoretically Optimal Sparse PCA<\/td><\/td>Jehyuk<\/td><\/td> (pdf)<\/span><\/span><\/a><\/td><\/tr>
<\/td><\/td> Sparse Principal Component Analysis (PCA) is a
\ndimensionality reduction technique wherein one seeks a lowrank representation of a data matrix with additional sparsity
\nconstraints on the obtained representation. We consider two
\nprobabilistic formulations of sparse PCA: a spiked Wigner and
\nspiked Wishart (or spiked covariance) model. We analyze an
\nApproximate Message Passing (AMP) algorithm to estimate the
\nunderlying signal and show, in the high dimensional limit, that
\nthe AMP estimates are information-theoretically optimal. As an
\nimmediate corollary, our results demonstrate that the posterior
\nexpectation of the underlying signal, which is often intractable to
\ncompute, can be obtained using a polynomial-time scheme. Our
\nresults also effectively provide a single-letter characterization of
\nthe sparse PCA problem.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

November 2014<\/p>\n\n\n\n

1<\/td><\/td>2014-11-03<\/td><\/td>Asif<\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
2<\/td><\/td>2014-11-10<\/td>Feature Extraction Approaches to RF Fingerprinting for Device Identification in Femtocells<\/td>jusung<\/td><\/td><\/td><\/tr>
<\/td><\/td><\/td>The problem of signaling storms caused by increased core network signaling load due to idle mode cell camping on femtocells is addressed and a novel approach to RF fingerprinting is proposed as a solution to tackle this problem by identifying device model types. This study also presents a novel feature extraction technique, which greatly improves identification accuracy over standard spectral approaches while limiting the number of random access channel (RACH) preambles required for identification. Accuracy of 99.8 percent was achieved. Importantly, the proposed technique can be implemented using today\u2019s low cost high-volume receivers and requires no manual performance tuning.<\/span><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

October 2014<\/p>\n\n\n\n

1<\/td><\/td>2014-10-13<\/td>Signal Recovery From Random Measurements<\/span>
\nVia Orthogonal Matching Pursuit<\/span><\/td>		Oliver<\/td>(DOC<\/a>)<\/td><\/td><\/tr>
<\/td><\/td><\/td>We discuss from this paper how to derive the theoretical probability of recovery for the orthogonal matching pursuit (OMP) algorithm. It is known that, till date, there is no theoretical phase transition analysis present for the OMP algorithms. The analysis presented in this paper could be used as a starting point to derive the phase transitions for the OMP algorithm. We discuss a key theorem and its proof in this talk.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

September 2014<\/p>\n\n\n\n

1<\/td><\/td>2014-09-22<\/td>Distributed Channel Coding for Underwater Acoustic
\nCooperative Networks<\/td>		Zafar Iqbal<\/td>(doc)<\/a><\/td> <\/a><\/td><\/tr>
<\/td><\/td><\/td>Multiuser cooperative schemes usually rely on relay selection or channel selection to avoid deep fading and achieve diversity while maintaining acceptable spectral efficiency. In some applications such as underwater acoustic communications, the low speed of the acoustic wave results in a very long delay between the channel state information (CSI) measurement time and the relay assignment time, which leads to a severely outdated CSI.<\/span>To remedy this, a distributed coding schemes that aim at achieving good diversity-multiplexing trade-off (DMT) for multiuser scenarios, where CSI is not available for resource allocation has been proposed. A network with multiple source nodes, multiple relay nodes, and a single destination is considered. First, a distributed linear block coding scheme, including Reed-Solomon codes, where each relay implements a column of the generator matrix of the code, and soft decision decoding is employed to retrieve the information at the destination side, is introduced. The end-to-end error performance of this scheme has been derived and shown that the achievable diversity equals the minimum Hamming distance of the underlying code, while its DMT outperforms that of existing schemes. Then the proposed scheme has been extended to distributed convolutional codes, and shown that achieving higher diversity orders is also possible.<\/b><\/span><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

August 2014<\/strong><\/p>\n\n\n\n

2<\/td><\/td>2014-08-26<\/td>Compressive Sensing for Sparse Touch Detector on Capacitive Touch Screens<\/td>Haeung Choi<\/td> Journal_Haeung_140826<\/a><\/td> <\/a><\/td><\/tr>
<\/td><\/td><\/td>Improved touch screen responsiveness and resolution can be achieved at the expense of the touch screen controller analog hardware complexity and power consumption. This paper proposes an alternative compressive sensing based approach to exploit the sparsity of simultaneous touches with respect to the number of sensor nodes to achieve similar levels of responsiveness. It is possible to reduce the analog data acquisitions complexity at the cost of extra digital computations with less total power consumption<\/b><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
1<\/td><\/td>2014-08-05<\/td>Compressed Sensing Based Fingerprint Identification for Wireless Transmitters<\/span><\/td>JuSung Kang<\/td>(DOC)<\/a><\/td> <\/a><\/td><\/tr>
<\/td><\/td><\/td>\n

This paper proposes a new method to identify different wireless transmitters based on compressed sensing. A data acquisition system is designed to capture the wireless transmitter signals. Complex analytical wavelet transform is used to obtain the envelope of the transient signal, and the corresponding features are extracted by using the compressed sensing theory. Feature selection utilizing minimumredundancy maximumrelevance (mRMR) is employed to obtain the optimal feature subsets for identification.<\/b><\/span><\/p>\n

The results show that the proposed method is more efficient for the identification of wireless transmitters with similar transient waveforms.<\/b><\/span><\/p>\n<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n


\nJuly 2014<\/strong><\/p>\n\n\n\n\n\n\n\n

1<\/td><\/td>2014-07-29<\/td>Precise Undersampling Theorems<\/span><\/td>Oliver<\/td>(doc)<\/a><\/td> <\/a><\/td><\/tr>
<\/td><\/td><\/td>\n
    \n
  • The Shannon\u2013Nyquist\u2013Kotelnikov\u2013Whittaker sampling theorem states precise number of measurements required to reconstruct any band limited signal. Undersampling theorems in Compressive sensing (CS) state that we may gather far fewer samples than the usual sampling theorem while exactly reconstructing the object of interest provided the object obeys a sparsity condition. <\/span><\/li>\n
  • While there are many ways to demonstrate such undersampling phenomena in CS, combinatorial geometry based approach is the only one mathematically rigorous approach to precisely quantify the true sparsity undersampling trade-off<\/span> curve of standard algorithms and standard compressed sensing matrices.<\/span><\/li>\n
  • The approach predicts the exact location in sparsity-undersampling domain where standard algorithms exhibit phase transitions in performance. <\/b><\/span><\/li>\n<\/ul>\n<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

    March 2014<\/strong><\/p>\n\n\n\n

     1<\/td><\/td>2014-03-11<\/td>Improved Successive Cancellation Decoding of Polar Codes<\/td><\/td>Jeong-Min Ryu<\/td> (pdf<\/a>)
    \n<\/a><\/td><\/tr>
    <\/td><\/td><\/td>A new decoding algorithm called the successive cancellation hybrid (SCH) is proposed. This proposed algorithm can provide a \ufb02exible con\ufb01guration when the time and space complexities are limited. Furthermore, a pruning technique is also proposed to lower the complexity by reducing unnecessary path searching operations. Performance and complexity analysis based on simulations shows that under proper con\ufb01gurations, all the three improved successive cancellation (ISC) decoding algorithms can approach the performance of the maximum likelihood (ML) decoding but with acceptable complexity.<\/span><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    2<\/td><\/td>2014-03-25<\/td>On some multiple integrals involving determinants<\/td>Oliver<\/td>(DOC)<\/a><\/td><\/td><\/tr>
    <\/td><\/td><\/td>This paper introduces a way to evaluate an N-dimensional integration whose integrand is a determinant of a matrix. This approach in this paper is general in nature and in particular, it is useful to derive eigenvalue distributions of Wishart matrices.<\/span><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    3<\/td><\/td>2014-04-01<\/td>Identifying Wireless User via Transmitter Imperfections<\/td>JuSung Kang<\/td><\/td>(DOC)<\/a><\/td><\/tr>
    <\/td><\/td><\/td>In this papers, the authors detail a first step toward a model based approach, which uses statistical models of RF transmitter components that are amenable for analysis. Algorithms based on statistical signal processing methods are developed to exploit non-linearities of wireless transmitters for the purpose of user identification in wireless systems. The decision rules are derived and their performance is analyzed. Results show that the proposed identification methods can be effective, even for short data records and relatively low signal-to-noise ratios. <\/span><\/td><\/tr>
    4<\/td><\/td>2014-05-13<\/td>Compressed Sensing Detector Design for Space Shift Keying in MIMO Systems<\/td><\/td>Haeung Choi<\/td>(<\/a>pdf)<\/a><\/td><\/tr>
    <\/td><\/td><\/td>A promising communication method, Massive MIMO is the large-scale multiple-input multiple-output(MIMO) technique which can achieve maximum diversity gain and large spatial multiplexing gain. However, massive MIMO also has many challenging problems such as heavy interference, expensive hardware cost, and large complexity of receiver. In spite of using generalized spatial shift keying(GSSK), which is one of methods to reduce the complexity of detection, the high complexity of optimal ML detector still becomes an obstacle. Author purposed a new (G)SSK detector applying the compressive sensing(CS) theory. Their detector, named normalized compressive sensing(NCS) (G)SSK detector can achieve considerable performance with low complexity compared with the optimal ML detector. <\/span><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    4<\/td><\/td>2014-05-27<\/td>Outage Probability of Cognitive Relay Networks with Interference Constraints<\/td><\/td>Zafar Iqbal<\/td>(<\/a>doc<\/a>)<\/td><\/tr>
    <\/td><\/td><\/td>The authors evaluate the outage probability of cognitive relay networks with cooperation between secondary users based on the underlay approach, while adhering to the interference constraint on the primary user. A relay selection criterion, suitable for cognitive relay networks, is provided, and using it, they derive the outage probability. It is shown that the outage probability of cognitive relay networks is higher than that of conventional relay networks due to the interference constraint. In addition, the outage probability is affected by the distance ratio of the interference link (between the secondary transmitter and the primary receiver) to the relaying link (between the secondary transmitter and the secondary receiver). They also prove that cognitive relay networks achieve the same full selection diversity order as conventional relay networks, and that the decrease in outage probability achieved by increasing the selection diversity (the number of relays) is not less than that in conventional relay networks.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n\n\n\n

    January 2014<\/strong><\/p>\n\n\n\n

     1<\/td><\/td>2014-01-28<\/td>Cooperative Spectrum Sensing with Transmit and Relay Diversity in Cognitive Radio Networks<\/td><\/td>Zafar Iqbal<\/td> (<\/a>DOC<\/a>)<\/td><\/tr>
    <\/td><\/td><\/td>The authors investigate the problem of cooperative spectrum sensing in cognitive radio (CR) networks over Rayleigh fading environment. The performance is analyzed by studying the error effects on the decision reporting and the results show that the cooperative spectrum sensing performance is limited by the probability of reporting errors.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    December 2013<\/strong><\/wp-block><\/strong><\/p>\n\n\n\n

     1<\/td><\/td>2013-12-09<\/td>Robust Compressive Data Gathering in Wireless Sensor Networks<\/td><\/td> Jongmok Shin<\/td> (<\/a>DOC)<\/a><\/td><\/tr>
    <\/td><\/td><\/td> In this paper, authors investigate the impact of outlying sensor readings and
    \nbroken links on high-fidelity data gathering, and propose approaches based on
    \nthe compressive sensing theory to identify outlying sensor readings and derive
    \nthe corresponding accurate values, and to infer broken links in Wireless Sensor
    \nNetworks.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    November 2013<\/strong><\/wp-block><\/p>\n\n\n\n

     1<\/td><\/td>2013-11-18<\/td>Missing-Area Reconstruction in Multispectral Images Under a Compressive Sensing Perspective<\/td><\/td> Hyeonghobaek<\/td> (DOC)<\/a><\/td><\/tr>
    <\/td><\/td><\/td> The intent of this paper is to propose new methods for the reconstruction of areas obscured by clods. They are based on compressive sensing theory, which allows finding sparse signal representations in underdetermined linear equation systems.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
     2<\/td>2013-11-25<\/td>Power and Channel Allocation for Cooperative Relay in Cognitive Radio Networks<\/td><\/td>Muhammad asif<\/td>(DOC)<\/a><\/td><\/tr>
    <\/td><\/td> In this paper authors mention that cognitive radio relay channels can be divided into three categories: direct, dual-hop, and relay channels. The relay node involves both dual-hop and relay diversity transmission. They develop power and channel allocation approaches for cooperative relay networks. They also develop a low complexity approach that can obtain most of the benefits from power and channel allocation with minor performance loss.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    October 2013<\/strong><\/wp-block><\/p>\n\n\n\n

    No<\/td><\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td><\/td>2013-10-14<\/td>The Exact support recovery of sparse signals with noise via orthogonal matching pursuit<\/td>Oliver<\/td>(DOC)<\/a><\/td><\/td><\/tr>
    <\/td><\/td><\/td>This letter derives sufficient conditions for the OMP to recover the support set of a sparse vector from noise corrupted measurements. In particular, the conditions are given in terms of the minimum absolute values of the signal amplitudes. That is, if the minimum values of the non-zero coefficient of the signal satisfies certain conditions then OMP guarantees exact support recovery.<\/td><\/tr>
    2<\/td><\/td>2013-10-21<\/td>Hierarchical and High-Girth QC LDPC codes<\/td>Jeongmin Ryu<\/td>(PDF)<\/a><\/td><\/td><\/tr>
    <\/td><\/td><\/td>They present an approach to designing capacity approaching high-girth low-density parity-check (LDPC) codes that are friendly to hardware implementation, and compatible with some desired input code structure defined using a protograph. The approach is based on a mapping of any class of codes defined using a protograph into a family of hierarchical quasi- cyclic (HQC) LDPC codes. Next, they present a girth-maximizing algorithm that optimizes the degrees of freedom within the family of codes to yield a high-girth HQC LDPC code, subject to bounds imposed by the fact that HQC codes are still quasi-cyclic. Finally, they discuss how certain characteristics of a code protograph will lead to inevitable short cycles and show that these short cycles can be eliminated using a \u201csquashing\u201d procedure that results in a high-girth QC LDPC code.<\/td><\/tr>
    3<\/td><\/td>2013-10-26<\/td>Ultra-Wideband Compressed Sensing : Channel Estimation<\/td>JuSung Kang<\/td> (DOC)<\/a><\/td><\/td><\/tr>
    <\/td><\/td><\/td>In this paper, they have introduced two novel ultra-wideband (UWB) channel estimation approaches based on compressive sensing (CS). The proposed approach relies on the fact that transmitting an ultra-short pulse through a multipath UWB channel leads to a received UWB signal that can be approximated by a linear combination of a few atoms from a pre-defined dictionary which means sparse representation of the received signal. The key in the proposed approach is in the design of a dictionary of parameterized waveforms (atoms) that closely matches the information-carrying pulse shape leading thus to higher energy compaction and sparse representation, and, therefore higher probability for CS reconstruction. In the first approach, the CS reconstruction capabilities are exploited to recover the composite pulse-multipath channel from a reduced set of random projections. This reconstructed signal is subsequently used as a referent template in a correlator-based detector. In the second approach, from a set of random projections of the received pilot signal, the Matching Pursuit algorithm is used to identify the strongest atoms in the projected signal that are related to the strongest propagation paths that composite the multipath UWB channel.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    August 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-08-01<\/td> Compressive Sensing for Spread Spectrum Receivers<\/td> Hyeongho<\/td> (pdf<\/a>)<\/td><\/td><\/tr>
    <\/td><\/td> This paper investigates the use of Compressive Sensing(CS) in a general Code Division Multiple Access (CDMA) receiver. They show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. Furthermore, they numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures.<\/td><\/tr>
    2<\/td>2013-08-08<\/td> Active illumination single-pixel camera based on compressive sensing<\/td> Eunseok<\/td>PPT<\/a><\/td><\/td><\/tr>
    <\/td><\/td>\n
    This paper is organized as follows. After a brief introduction (Section 1), some mathematical back- ground essential to the understanding of CS is shown (Section 2). Then, in Section 3, CS is presented along with some of its principal properties. Section 4 explains why the \u21131-norm is such a good option for com- pressive sensing. Some insights about the robustness of CS in the presence of noise are given in Section 5. Next, in Section 6, the single-pixel camera developed at Rice University is discussed. Subsequently, the innovative active illumination single-pixel camera developed in the scope of the current work is described. Following that, experimental results from the single-pixel cameras are presented. In the end, the main conclusions of this work are exposed.<\/div>\n<\/td><\/tr>
    3<\/td>2013-08-29<\/td> Resource Allocation in Cognitive Radio Relay Networks<\/td>Muhammad Asif<\/td> (PDF)<\/a><\/td><\/td><\/tr>
    <\/td><\/td> In this paper authors formulate the problem of Resource Allocation (RA) in Cognitive Radio (CR) networks with relay stations. The problem takes into account the issues like: fluctuations of usable spectrum resource, channel quality variations caused by frequency selectivity, and interference caused by different transmit power levels. They propose easy to implement heuristic algorithms. The simulation results reveal that presented solutions show good proportional fairness among CR users and improvement in system throughput by power control.<\/td><\/tr>
    4<\/td>2013-09-16<\/td> Multipath Matching Pursuit<\/td> Hwanchol Jang<\/td> (pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td> In this paper, they propose an algorithm referred to as multipath matching pursuit that investigates multiple promising candidates to recover sparse signals from compressed measurements. Their method is inspired by the fact that the problem to find the candidate that minimizes the residual is readily modeled as a combinatoric tree search problem and the greedy search strategy is a good fit for solving this problem. In the empirical results as well as the restricted isometry property (RIP) based performance guarantee, they show that the proposed MMP algorithm is effective in reconstructing original sparse signals for both noiseless and noisy scenarios.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    July 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-07-11<\/td>A sparse signal reconstruction perspective for source localization with sensor arrays<\/td>J.Oliver<\/td> (pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>In this paper, the authors present a source localization method based on sparse representation of sensor measurements. In particular, they use SVD of the data matrix obtained from the sensors to summarize the multiple measurements. The SVD summarized data is then sparsely represented in order to detect the sources. The authors also proposed grid refinement in order to mitigate the effects of limiting estimates to a grid of spatial locations. They demonstrate the superior resolution ability with limited time samples of their method over the existing methods via various experiments.<\/td><\/tr>
    2<\/td>2013-07-18<\/td>Enhancing Iterative Decoding of Cyclic LDPC Codes Using Their Automorphism Groups<\/td> Jeongmin<\/td>(pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>In this paper they focus on cyclic LDPC codes defined by a circulant parity-check matrix and consider two known subgroups of the automorphism group of a cyclic code. For the large class of idempotent-based cyclic LDPC codes in the literature, they show that the two subgroups only provide equivalent parity-check matrices and thus cannot be harnessed for iterative decoding. Towards exploiting the automorphism group of a code, they propose a new class of cyclic LDPC codes based on pseudo-cyclic MDS codes with two information symbols,for which nonequivalent parity-check matrices are obtained. Simulation results show that for our constructed codes of short lengths, the automorphism group can significantly enhance the iterative decoding performance.<\/td><\/tr>
    3<\/td>2013-07-25<\/td> Multiuser detection of sparsely spread CDMA<\/td>Jaewook<\/td>PDF<\/a><\/td><\/td><\/tr>
    <\/td><\/td>Abstract: This paper has discussed about design and analysis of multiuser
    \ndetection (MUD) using sparsely spread CDMA systems. The objective of
    \nthe MUD problem is how to detect multiple user signals
    \nsimultaneously at the low computational cost. The main obstacle is
    \nmultiple-access interference (MAI). These multiple user signals are
    \ninterference for each user detection one another. The MAI problem
    \narise in most CDMA systems, and optimal detection in such systems
    \nrequires exponentially growing computation as the number of user
    \nincreases. But a good news is that the simultaneous users in time is
    \nvery few. Therefore, this paper investigates a suboptimal MUD
    \ndetection using sparse CDMA systems. The key idea of the proposed
    \nsystem is to encode the transmitted waveforms using sparse spread
    \nCDMA codes and detect the signal using a linear-complexity belief
    \npropagation (BP) algorithm. We summarize the contributions of this
    \nwork is following:
    \n– Description the sparse CDMA system
    \n– Properties of the sparsly spread CDMA codes for the convergence of the BP algorithm
    \n– Design of the BP algorithm for the MUD problem
    \n– Asymptotic analysis of performance of the BP algorithm based MUD detection
    \nIn this report, we aim to sketch the key point of each contribution
    \nof this paper.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    June 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-06-13<\/td> Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit<\/td>Sangjun<\/td> pdf<\/a><\/td>pdf<\/a> 
    \n<\/a><\/td><\/tr>
    <\/td><\/td>This paper is to show that a sufficient condition on the number of measurements for a successful greedy algorithm called Orthogonal Matching Pursuit. We understand how the authors of this paper derive their sufficient condition.<\/td><\/tr>
    2<\/td> 2013-06-27<\/td>Link Status Monitoring Using Network Coding<\/td> Jin-Taek<\/td> (pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td> This work has presented a novel approach to link status monitoring based on a deterministic approach that exploits linear network coding at the internal nodes in a network. The key problem of identi\ufb01ability for such approaches was highlighted and various insights provided regarding this concept. New suf\ufb01cient conditions were derived for successfully identifying a congested link in any logical network, and tradeoffs between length of training slots and size of the network coding alphabet established.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    May 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-05-09<\/td> Turbo Reconstruction of Structured Sparse Signal<\/td>Hyeongho<\/td>  pdf<\/a><\/td> 
    \n<\/a><\/td><\/tr>
    <\/td><\/td>This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by hidden binary pattern, and a structured probabilistic prior (e.g., Markov random chain\/field\/tree) is assumed on the pattern. Exact inference is discussed and an approximate inference scheme, based on loopy belief propagation (BP), is proposed. The proposed scheme iterates between exploitation of the observation-structure and exploitation of the pattern-structure, and is closely related to noncoherent turbo equalization, as used in digital communication receivers. An algorithm that exploits the observation structure is then detailed based on approximate message passing ideas.<\/td><\/tr>
    2<\/td>2013-05-16<\/td>Compressive fluorescence microscopy for biological hyperspectral imaging<\/td>Eunseok<\/td>PDF<\/a><\/td><\/td><\/tr>
    <\/td><\/td> The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices\u2014especially in optics\u2014have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells, and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher-dimensional signals, which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point to some remaining challenges for CS fluorescence microscopy.<\/td><\/tr>
    3<\/td>2013-05-30<\/td> Simplified Relay Selection and Power Allocation in Cooperative Cognitive Radio Systems<\/td>Muhammad Asif<\/td> PDF<\/a>
    \n<\/a><\/td>    		<\/td><\/tr>
    <\/td><\/td> In this paper authors propose solution of a combined problem; relay selection and power allocation to secondary users under the constraint of limited interference to primary users in cognitive radio (CR) system. Objective of the joint problem was to maximize system throughput. A high complexity optimal solution and a low complexity suboptimal solution are proposed. The presented solutions show over 50% improvement in system throughput.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    April 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-04-11<\/td>Efficient Design and Decoding of Polar Codes<\/span><\/td>Jeongmin<\/span><\/td> (PDF)<\/a><\/td> 
    \n<\/a><\/td><\/tr>
    <\/td><\/td>Polar codes are shown to be instances of both generalized concatenated codes and multilevel codes. It is shown that the performance of a polar code can be improved by representing it as a multilevel code and applying the multistage decoding algorithm with maximum likelihood decoding of outer codes. Additional performance improvement is obtained by replacing polar outer codes with other ones with better error correction performance. In some cases this also results in complexity reduction. It is shown that Gaussian approximation for density evolution enables one to accurately predict the performance of polar codes and concatenated codes based on them.<\/span><\/td><\/tr>
    2<\/td>2013-04-18<\/td>Aliasing-Free Wideband Beamforming Using Sparse Signal Representation<\/span><\/td> J. Oliver<\/span><\/td>   (PDF)<\/a>
    \n<\/a><\/td>    		<\/td><\/tr>
    <\/td><\/td>This paper considers the use of sparse signal representation for the wideband direction of arrival (DOA) or angle of arrival estimation problem. In particular, this paper discusses about the two ambiguities, namely, spatial and algebraic aliasing that arise in wideband-DOA. The authors of the paper suggest procedures to avoid the aliasing using multiple measurement vector and multiple dictionaries.<\/span><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    March 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-03-21<\/td>Scaling Up MIMO: Opportunities and challenges with very large arrays<\/td>Woongbi<\/td><\/td> (PDF)<\/a><\/td><\/tr>
    <\/td><\/td>Very large MIMO systems, also known as massive MIMO, multiuser MIMO systems, or large-scale antennas systems is an emerging research area in antenna systems, electronics, and wireless communication systems. A base station with an antenna array serves a multiplicity of single-antenna terminals. In this presentation, the fundamental principle of massive MIMO technology and several issues are introduced.<\/td><\/tr>
    2<\/td>2013-03-28<\/td> Shrinkage methods to linear regression problems<\/td>Jaewook<\/td> (PDF)<\/a><\/td> (PDF)<\/a><\/td><\/tr>
    <\/td><\/td>In this chapter, we are interested in the linear regression with shrinkage methods. The shrinkage method have got attention to solve the problem of linear systems y=Ax because the method provides very flexible solver from dense signals to sparse signals. First, we will introduce basic concept of two shrinkage methods in the linear regression, Ridge and Lasso. Then, we move the focus to sparse recovery with Lasso and its variants for different problem setting such as Fused lasso and Elastic net.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    February 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-02-07<\/td>Performance Analysis of Iterative Decoding Algorithms with Memory over Memoryless Channels<\/td>Jeongmin<\/td>    (PDF)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>In this work, they propose a model for iterative decoding algorithms with memory which covers successive relaxation (SR) version of belief propagation and differential decoding with binary message passing (DD-BMP) algorithms. Based on this model, they derive a Bayesian network for iterative algorithms with memory over memoryless channels and use this representation to analyze the performance of the algorithms using density evolution.<\/td><\/tr>
    2<\/td>2013-02-14<\/td> Faster STORM using compressed sensing<\/td>Eunseok<\/td> (PDF)<\/a><\/td><\/td><\/tr>
    <\/td><\/td> In super-resolution microscopy methods based on single-molecule switching, the rate of accumulating single-molecule activation events often limits the time resolution. Here we developed a sparse-signal recovery technique using compressed sensing to analyze images with highly overlapping fluorescent spots. This method allows an activated fluorophore density an order of magnitude higher than what conventional single-molecule fitting methods can handle. Using this method, we demonstrated imaging microtubule dynamics in living cells with a time resolution of 3 s.<\/td><\/tr>
    3<\/td>2013-02-21<\/td>A Node-Based Time Slot Assignment Algorithm for STDMA Wireless Mesh Networks<\/td>Muhammad Asif Raza<\/td>(PDF)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>In this paper authors present a link capacity model for spatial time-division multiple access (STDMA) mesh networks. It makes use of a simplified transmission model that also considers channel fading. The model then forms the basis of a node-based slot-assignment and scheduling algorithm. This algorithm enables the user to exploit multiuser diversity that results in optimizes network throughput. The presented algorithm shows significant improvement in the throughput when compared with existing slot-assignment methods.<\/td><\/tr>
    4<\/td>2013-02-28<\/td> A New TwIST: Two-Step Iterative Shrinkage\/Thresholding Algorithms for Image Restoration<\/td>Hwanchol Jang<\/td> (pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>In this paper, the authors introduces TwIST algorithms, exhibiting much faster convergence rate than IST for ill-conditioned problems. For a vast class of nonquadratic convex regularizers, they show that TwIST converges to a minimizer of the objective function, for a given range of values of its parameters.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    January 2013<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    1<\/td>2013-01-10<\/td> Statistical physics-based re construction in compressed sensing<\/td>Jaewook Kang<\/td>(pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>In this report, the author introduces a expectation maximization (EM) based belief propagation algorithm (BP) for sparse recovery, named EM-BP. The algorithm have been mainly devised by Krzakala et al. from ParisTech in France. The properties of EM-BP are as given below:1) It is A low-computation approach to sparse recovery,<\/span>

    <\/p>\n

    2) It works well without the prior knowledge of the signal,<\/span><\/p>\n

    3) It overcomes the l1 phase transition given by Donoho and Tanner  under the noiseless setup,<\/span><\/p>\n

    4) It is further improved in conjunction with seeding matrices (or spatial coupling matrices).<\/span><\/p>\n

    The main purpose of this report regenerates a precise description of EM-BP derivation from the reference paper. It might be very helpful for understanding of EM-BP algorithm, and an answer for such a question: How and why does the algorithm work ? Therefore, we will focus on the explanation of 1) and 2) in the properties, and just show the result of the paper with respect to that of 3) and 4).<\/span><\/p>\n<\/td><\/tr>

    2<\/td>2013-01-24<\/td>A fast approach for over-complete sparse decomposition based on smoothed L0 norm<\/td>Oliver<\/td>  (PDF)<\/a><\/td> (PDF)<\/a><\/td><\/tr>
    <\/td><\/td>This paper proposes a fast algorithm for overcomplete sparse decomposition.The algorithm is derived by directly minimizing the L0 norm after smoothening. Hence, the algorithm is named as smoothed L0 (SL0) algorithm.  The authors demonstrate that their algorithm is 2-3 orders of magnitude faster than the state-of-the-art interior point solvers with same (or better) accuracy.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    2012-December<\/strong><\/wp-block><\/p>\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
     1<\/td>2012-12-13<\/td>Multiuser Cooperative Diversity Through Network Coding Based on Classical Coding Theory<\/td>Jintaek Seong<\/td> (pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>To increase the diversity order of cooperative wireless communication systems without sacrificing the system’s rate, they propose the generalized dynamic-network code (GDNC). They showed that the problem of designing network codes that maximize the diversity order is related to that of designing optimal linear block codes, in the Hamming distance sense, over a nonbinary finite fields.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

    2012-November<\/strong><\/p>\n\n\n\n\n\n\n\n

    No<\/td>Date<\/td>Title<\/td>Speaker<\/td>Presentation<\/td>Discussion<\/td><\/tr>
    4<\/td>2012-11-29<\/td>Compressive sensing and its application in wireless sensor network & correlated signal recovery method<\/td>Jaegun Choi<\/td> (pdf)<\/a><\/td> (pdf)<\/a><\/td><\/tr>
    <\/td><\/td>The thesis of master degree: In this paper, we discuss the application of a new compression technique called compressive sensing (CS) in wireless sensor networks (WSNs). CS is a signal acquisition and compression framework recently developed in the field of signal processing and information theory. We applied this CS technique to WSN which consists of a large number of wireless sensor nodes and a central fusion center (FC). This CS based signal acquisition and compression is done by a simple linear projection at each sensor node. Then, each sensor transmits the compressed samples to the FC. The FC which collects the compressed signals from the sensors jointly reconstructs the signals in polynomial time using a signal recovery algorithm. The distributed sensors observe similar event in designated region. Therefore, the observed signals have considerable correlation each other. We pay some effort in modeling correlation between the signals acquired from the sensors. After modeling the correlated signals, we propose POMP (Phased-OMP) which can recover any type of correlated signals stably and effectively. We introduce the idea of our proposed algorithm in detail and then compare the reconstruction performance of POMP with previous algorithms<\/td><\/tr>
    3<\/td>2012-11-22<\/td>Capacity of OFDM Systems over Fading Underwater Acoustic Channels<\/td>Zafar Iqbal<\/td>(pdf)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>This paper derives the upper and lower bounds for channel capacity of the OFDM systems over underwater acoustic channels as a function of distance between the transmitter and the receiver. It incorporates frequency dependent path loss at each arrival path at the receiver due to acoustic propagation. This leads the UW channel to be modeled as wide sense stationary and correlated scattering (WSS-non-US) fading channel. Results from both Rayleigh and Rician fading show a gap between the upper and lower bounds which depends, not only on the ranges and shape of the scattering function of the UW channel but also on the distance between the transmitter and the receiver.<\/td><\/tr>
    2<\/td>2012-11-15<\/td>On the Recovery Limnit of Sparse Signals Using Orthogonal Matching Pursuit<\/td>Sangjun Park<\/td>(pdf)<\/a><\/td>(pdf)<\/a><\/td><\/tr>
    <\/td><\/td>In the paper, the authors give a sufficient condition of the Orthogonal matching Pursuit (OMP) algorithm. In [2], Wakin and Davenport inisted that OMP can reconstrcut any K sparse signal if delta_(K+1) < 1\/(3*sqrt(K)). However, in this talk, an improved sufficient condition that guarantees the perfec recovery of OMP is presented.<\/td><\/tr>
    1<\/td>2012-11-08<\/td>Fair Bandwidth Allocation in Wireless Mesh Networks With Cognitive Radios<\/td>Muhammad Asif Raza<\/td>(PDF)<\/a><\/td><\/td><\/tr>
    <\/td><\/td>In this paper authors discuss about fair bandwidth allocation issue in wireless mesh networks with cognitive radios. In order to achieve fairness they define the two allocation problems based upon a simple max-min fairness model and lexicographical max-min fairness model. They solve the allocation problems by using linear programming based heuristic algorithms. The proposed algorithms ensure both fairness and throughput. The presented algorithms are evaluated for their effectiveness and fairness based upon extensive simulations.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n

     <\/strong><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

    Information and System Group seminar materials November 2015 1 2015-11-27 Support recovery with orthogonal matching pursuit in the presence of noise Jang Jehyuk PDF In this paper, sufficient and necessary conditions for correct recovery of sparse solution using OMP under the noisy environment are provided. In addition, upper bound of the error rate in recovering is …<\/p>\n","protected":false},"author":74,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,26],"tags":[],"class_list":["post-28714","post","type-post","status-publish","format-standard","hentry","category-laboratory-life","category-research"],"_links":{"self":[{"href":"https:\/\/heungno.net\/index.php?rest_route=\/wp\/v2\/posts\/28714","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/heungno.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/heungno.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/heungno.net\/index.php?rest_route=\/wp\/v2\/users\/74"}],"replies":[{"embeddable":true,"href":"https:\/\/heungno.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=28714"}],"version-history":[{"count":3,"href":"https:\/\/heungno.net\/index.php?rest_route=\/wp\/v2\/posts\/28714\/revisions"}],"predecessor-version":[{"id":28961,"href":"https:\/\/heungno.net\/index.php?rest_route=\/wp\/v2\/posts\/28714\/revisions\/28961"}],"wp:attachment":[{"href":"https:\/\/heungno.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28714"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/heungno.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28714"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/heungno.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28714"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}