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Cihan Tepedelenlioglu
Arizona State University
230Publications
26H-index
3,530Citations
Publications 230
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#1Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
This short note provides a large-deviation-based upper bound on the growth rate of directed last passage percolation (LPP) using the entropy of the normalized direction vector.
#1Ruochen Zeng (ASU: Arizona State University)H-Index: 3
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
Abstract Device-to-Device (D2D) communications has been proposed to provide high data rate service via direct transmissions between devices. Cooperation between the cellular user (CU) and the D2D user can be achieved using superposition coding, where the D2D transmitter (DT) allocates some of its transmission power to forward the CU’s traffic, and transmits to its own D2D receiver (DR) with the remaining power. The sum rate of the cellular and D2D networks in existing schemes are limited by allo...
#1Suhas Ranganath (ASU: Arizona State University)H-Index: 7
#2Jayaraman J. Thiagarajan (ASU: Arizona State University)H-Index: 10
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
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In this paper, we present a unique Android-DSP (AJDSP) application which was built from the ground up to provide mobile laboratory and computational experiences for educational use. AJDSP provides a mobile intuitive environment for developing and running signal processing simulations in a user-friendly. It is based on a block diagram system approach to support signal generation, analysis, and processing. AJDSP is designed for use by undergraduate and graduate students and DSP instructors. Its ex...
#1Ahmed EwaishaH-Index: 4
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
In this work we study the problem of hard-deadline constrained data offloading in cellular networks. A single-Base-Station (BS) single-frequency-channel downlink system is studied where users request the same packet from the BS at the beginning of each time slot. Packets have a hard deadline of one time slot. The slot is divided into two phases. Out of those users having high channel gain allowing them to decode the packet in the first phase, one is chosen to rebroadcast it to the remaining user...
#1Henry Braun (ASU: Arizona State University)H-Index: 4
#2Pavan K. Turaga (ASU: Arizona State University)H-Index: 21
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
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Abstract Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors includi...
#1Sunil RaoH-Index: 2
#2Andreas SpaniasH-Index: 26
Last.Cihan TepedelenliogluH-Index: 26
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May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Raksha Ramakrishna (ASU: Arizona State University)H-Index: 1
#2Anna Scaglione (ASU: Arizona State University)H-Index: 45
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
view all 4 authors...
In this paper, we present a distributed array processing algorithm to analyze the power output of solar photo-voltaic (PV) installations, leveraging the low-rank structure inherent in the data to estimate possible faults. Our multi-agent algorithm requires near-neighbor communications only and is also capable of jointly estimating the common low rank cloud profile and local shading of panels. To illustrate the workings of our algorithm, we perform experiments to detect shading faults in solar PV...
May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
#1Jie Fan (ASU: Arizona State University)
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 26
view all 3 authors...
We propose a novel graph filtering method for semi-supervised classification that adopts multiple graph shift matrices to obtain more flexibility in dealing with misleading features. The resulting optimization problem is solved with a computationally efficient alternating minimization approach. In simulation experiments, we implement both conventional and our proposed graph filters as semi-supervised classifiers on real and synthetic datasets to demonstrate advantages of our algorithms in terms ...
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