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Cihan Tepedelenlioglu
Arizona State University
229Publications
26H-index
3,881Citations
Publications 228
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#1Gowtham Muniraju (ASU: Arizona State University)H-Index: 3
#2Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
Last.Andreas Spanias (ASU: Arizona State University)H-Index: 28
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A novel distributed algorithm for estimating the maximum of the node initial state values in a network, in the presence of additive communication noise is proposed. Conventionally, the maximum is estimated locally at each node by updating the node state value with the largest received measurements in every iteration. However, due to the additive channel noise, the estimate of the maximum at each node drifts at each iteration and this results in nodes diverging from the true max value. Max-plus a...
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#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...
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#1Suhas Ranganath (ASU: Arizona State University)H-Index: 7
#2Jayaraman J. Thiagarajan (ASU: Arizona State University)H-Index: 14
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...
An increase in grid-connected photovoltaic arrays creates a need for efficient and reliable fault detection. In this paper, machine learning strategies for fault detection are presented. An Artificial Neural Network was studied with the goal of detecting three photovoltaic module conditions. In addition, an unsupervised approach was successfully implemented using the -means clustering algorithm, successfully detecting arc and ground faults. To distinguish and localize additional faults such as s...
1 CitationsSource
Ground-based sky imaging has won popularity due to its higher temporal and spatial resolution when compared with satellite or air-borne sky imaging systems. Cloud identification and segmentation is the first step in several areas, such as climate research and lately photovoltaic power generation forecast. Cloud-sky segmentation involves several variables including sun position and type and altitude of clouds. We proposed a training-free cloud/sky segmentation based on a threshold that adapts to ...
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Last.Farib KhondokerH-Index: 1
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#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: 6
#2Pavan Turaga (ASU: Arizona State University)H-Index: 24
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...
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#1Vivek Sivaraman Narayanaswamy (ASU: Arizona State University)H-Index: 1
#2Raja Ayyanar (ASU: Arizona State University)H-Index: 25
Last.Devarajan SrinivasanH-Index: 5
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A cyber-physical system (CPS) approach for optimizing the output power of photovoltaic (PV) energy systems is proposed. In particular, a novel connection topology reconfiguration strategy for PV arrays to maximize power output under partial shading conditions using neural networks is put forth. Depending upon an irradiance/shading profile of the panels, topologies, namely series parallel (SP), total cross tied (TCT) or bridge link (BL) produce different maximum power points (MPP). The connection...
1 CitationsSource
#1Sunil Rao (ASU: Arizona State University)H-Index: 4
#2Andreas Spanias (ASU: Arizona State University)H-Index: 28
Last.Cihan Tepedelenlioglu (ASU: Arizona State University)H-Index: 26
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In this paper, we describe a Cyber-Physical system approach to fault detection in Photovoltaic (PV) arrays. More specifically, we explore customized neural network algorithms for fault detection from monitoring devices that sense data and actuate at each individual panel. We develop a framework for the use of feedforward neural networks for fault detection and identification. Our approach promises to improve efficiency by detecting and identifying eight different faults and commonly occurring co...
4 CitationsSource
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