Browsing by Subject "Signal processing"
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Item Analysis of Signal Intelligence for Wireless Communication Systems(2016-05-12) Kovuri, Nishant; Pendyala, SuryaDev; Dawood, Suhana; Mohammed, Abubakar S; Zheng, David; Computer ScienceThis research is based upon derivations of signal data in wireless communication systems. A cellular network is a radio network distributed over land through cells where each cell includes a fixed location transceiver known as base station. These cells together provide radio coverage over large geographical areas. Mobile network is a combination of multiple nodes (MS, BTS, BSC, MSC, HLR, VLR, EIR) Widely known problems are due to medium between sender-receiver, terrain area, overload traffic, TCP/IP packets congestion, transmitter-receiver hardware and other criteria. Problems could be tackled and reduced in different approaches based on causes.Item A Chromatic Dispersion Estimation Method for Arbitrary Modulation Formats(Optical Society of America, 2011-05-06) Zweck, John; Menyuk, CurtisSimulations show that a modulation-format-independent method for estimating chromatic dispersion from the phase of a coherently-received signal at four frequencies can estimate 3000 ps/nm of dispersion to within 2% at an OSNR of 10 dB.Item Provable Randomized Coordinate Descent for Matrix Completion(IEEE, 2024-03-18) Callahan, Matthew; Vu, Trung; Raich, RavivLow-rank matrix completion, the process of estimating a low-rank matrix from a small subset of its entries, has many applications including collaborative filtering and system identification. Many algorithms have been considered to address this problem. Coordinate descent has been previously proposed to tackle scalability both in terms of runtime and space complexity. Due to the use of regularization in the method, the method provides no convergence guarantees. Additionally, the choice of the regularization parameter can significantly affect the algorithm performance. Here, we study a regularization-free randomized coordinate descent method that uses an efficient periodic refactorization to guarantee a linear convergence rate. To support the proposed algorithm, we provide an analysis of the algorithm asymptotic convergence rate alongside a per-iteration computational complexity analysis. Using numerical experiments, we verify the correctness of our analysis and illustrate the overall computation advantage of the proposed approach.Item Subgroup Identification Through Multiplex Community Structure Within Functional Connectivity Networks(IEEE, 2024-03-18) Yang, H.; Ortiz-Bouza, M.; Vu, Trung; Laport, Francisco; Calhoun, V. D.; Aviyente, S.; Adali, TulaySubgroup identification is a fundamental step in precision medicine. Recent research applying data-driven methods such as independent component/vector analysis to multi-subject functional magnetic resonance imaging (fMRI) data has effectively revealed meaningful subgroups. These methods typically focus on single-dimensional information, such as individual functional networks or assuming uniform subgroup structures across networks. Given the complex nature of psychiatric disorders, considering the relationships among subjects across different functional networks can offer valuable insights into diagnostic heterogeneity. We introduce a novel subgroup identification method that leverages multiplex community detection to identify subgroups from multi-subject resting-state fMRI data. The proposed method models subject correlations across functional networks as a multiplex network and identifies common communities across multiple networks and unique communities specific to each functional network. Results from applying the proposed method to 464 psychotic patients show that the identified subgroups exhibit significant group differences on multiple meaningful functional networks as well as the clinical scores, which demonstrate the effectiveness of our method on identifying meaningful subgroups.