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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Quartile: search.filters.quartile.Q1Subject: search.filters.subject.Electrical electronic engineering

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    End-to-end deep multi-modal physiological authentication with smartbands
    (IEEE-Inst Electrical Electronics Engineers Inc, 2021) Ekiz, Deniz; Dardağan, Yağmur Ceren; Aydar, Furkan; Köse, Rukiye Dilruba; Ersoy, Cem; N/A; Can, Yekta Said; Researcher; College of Social Sciences and Humanities; N/A
    The number of fitness tracker users increases every day. Most of the applications require authentication to protect privacy-preserving operations. Biometrics such as face images have been used widely as login tokens, but they have privacy issues. Moreover, occlusions like face masks used for COVID may reduce their effectiveness. Smartbands can track heart rate, movements, and electrodermal activities. They have been widely used for health-related applications. The use of smartbands for authentication is in the exploratory stage. Physiological signals gathered from smartbands may be used to create a multi-modal and multi-sensor authentication system. The popularity of smartbands enables us to deploy new applications without a need to buy additional hardware. In this study, we explore the multi-modal physiological biometrics with end-to-end deep learning and feature-based traditional systems. We collected multi-modal physiological data of 80 people for five days using modern smartbands. We applied a deep learning approach to the multi-modal physiological data and used feature-based traditional machine learning classifiers. The CNN-LSTM model achieved a 9.31% equal error rate and outperformed other models in terms of authentication performance.
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    A physical channel model for nanoscale neuro-spike communications
    (IEEE-Inst Electrical Electronics Engineers Inc, 2013)  Balevi, eren; Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 6647
    Nanoscale communications is an appealing domain in nanotechnology. Novel nanoscale communications techniques are currently being devised inspired by some naturally existing phenomena such as the molecular communications governing cellular signaling mechanisms. Among these, neuro-spike communications, which governs the communications between neurons, is a vastly unexplored area. The ultimate goal of this paper is to accurately investigate nanoscale neuro-spike communications characteristics through the development of a realistic physical channel model between two neurons. The neuro-spike communications channel is analyzed based on the probability of error and delay in spike detection at the output. The derived communication theoretical channel model may help designing novel artificial nanoscale communications methods for the realization of future practical nanonetworks, which are the interconnections of nanomachines.
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    Guest editorial special issue on toward securing Internet of Connected Vehicles (IoV) from virtual vehicle hijacking
    (Institute of Electrical and Electronics Engineers (IEEE), 2019) Cao, Yue; Kaiwartya, Omprakash; Song, Houbing; Lloret, Jaime; Ahmad, Naveed; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211
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    Information theoretical optimization gains in energy adaptive data gathering and relaying in cognitive radio sensor networks
    (IEEE-Inst Electrical Electronics Engineers Inc, 2012) N/A; Department of Electrical and Electronics Engineering; Gülbahar, Burhan; Akan, Özgür Barış; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; 234525; 6647
    Cognitive radio (CR) technology helps mitigate wireless resource scarcity problem by dynamically changing frequency spectrum, power and modulation type. Opportunistic spectrum access increases the network capability and quality. Recently, CR applied to wireless sensor networks (WSNs) generated the paradigm of cognitive radio sensor networks (CRSNs) overcoming the challenges posed by event-driven traffic demands of WSNs. To realize advantages of CRSN, spectrum and power allocation, and routing must be jointly considered to maximize the information capacity, resource utilization and the lifetime. In this paper, power and rate adaptation problem is analyzed for a multi-hop CRSN in an information theoretical (IT) capacity maximization framework combined with energy adaptive (EA) mechanisms and utilization of sensor data information correlations (ICs). CRSN characteristics, i.e., fast data aggregation, bursty traffic and node failures, are considered. The capacity optimization problem is defined analytically and practical local schemes are presented showing the superiority of objective functions utilizing ICs and EA mechanisms in terms of the resulting maximum information rate at sink, i.e., R-max, lifetime, and energy utilization. Furthermore, dependence of performance on total bandwidth and various relay energy distributions is explored observing the logarithmic dependence of R-max on total bandwidth.
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    Intravehicular energy-harvesting wireless networks reducing costs and emissions
    (IEEE-Inst Electrical Electronics Engineers Inc, 2017) Sangiovanni-Vincentelli, Alberto; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211
    Vehicles have mutated from mechanical systems into cyberphysical systems featuring a large number of electronic control units (ECUs), sensors, and actuators. The wiring harnesses used for the transmission of data and power delivery for these components may have up to 4,000 parts, weigh as much as 40 kg, and contain up to 4 km of wiring. The amount of wiring is expected to grow as vehicles evolve and begin to include enhanced active safety features and, eventually, self-driving capabilities and diversified sensing resources. Consequently, the ability to eliminate wires in vehicles is a compelling value proposition; it decreases part, manufacturing, and maintenance costs and improves fuel efficiency and, therefore, greenhouse gas emissions. Furthermore, it may spur innovation by providing an open architecture to accommodate new components, offering the potential for growth in automotive applications-possibly similar to the computer and phone industry over the past decade.
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    Using synthetic data for person tracking under adverse weather conditions
    (Elsevier, 2021) Kerim, Abdulrahman; Çelikcan, Ufuk; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331
    Robust visual tracking plays a vital role in many areas such as autonomous cars, surveillance and robotics. Recent trackers were shown to achieve adequate results under normal tracking scenarios with clear weather conditions, standard camera setups and lighting conditions. Yet, the performance of these trackers, whether they are corre-lation filter-based or learning-based, degrade under adverse weather conditions. The lack of videos with such weather conditions, in the available visual object tracking datasets, is the prime issue behind the low perfor-mance of the learning-based tracking algorithms. In this work, we provide a new person tracking dataset of real-world sequences (PTAW172Real) captured under foggy, rainy and snowy weather conditions to assess the performance of the current trackers. We also introduce a novel person tracking dataset of synthetic sequences (PTAW217Synth) procedurally generated by our NOVA framework spanning the same weather conditions in varying severity to mitigate the problem of data scarcity. Our experimental results demonstrate that the perfor-mances of the state-of-the-art deep trackers under adverse weather conditions can be boosted when the avail-able real training sequences are complemented with our synthetically generated dataset during training. (c) 2021 Elsevier B.V. All rights reserved.
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    Compressed training adaptive equalization: algorithms and analysis
    (IEEE-Inst Electrical Electronics Engineers Inc, 2017) Yılmaz, Baki B.; Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 41624
    We propose "compressed training adaptive equalization" as a novel framework to reduce the quantity of training symbols in a communication packet. It is a semi-blind approach for communication systems employing time-domain/frequency-domain equalizers, and founded upon the idea of exploiting the magnitude boundedness of digital communication symbols. The corresponding algorithms are derived by combining the leasts-quares- cost-function measuring the training symbol reconstruction performance and the infinity-norm of the equalizer outputs as the cost for enforcing the special constellation boundedness property along the whole packet. In addition to providing a framework for developing effective adaptive equalization algorithms based on convex optimization, the proposed method establishes a direct link with compressed sensing by utilizing the duality of the l(1) and l(infinity) norms. This link enables the adaptation of recently emerged l(1)-norm-minimization-based algorithms and their analysis to the channel equalization problem. In particular, we show for noiseless/low noise scenarios, the required training length is on the order of the logarithm of the channel spread. Furthermore, we provide approximate performance analysis by invoking the recent MSE results from the sparsity-based data processing literature. Provided examples illustrate the significant training reductions by the proposed approach and demonstrate its potential for high bandwidth systems with fast mobility.
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    Stationary point characterization for a class of BCA algorithms
    (IEEE-Inst Electrical Electronics Engineers Inc, 2017) İnan, Hüseyin A.; Cruces, Sergio; Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 41624
    Bounded component analysis (BCA) is a recently introduced approach including independent component analysis as a special case under the assumption of source boundedness. In this paper, we provide a stationary point analysis for the recently proposed instantaneous BCA algorithms that are capable of separating dependent, even correlated as well as independent sources from their mixtures. The stationary points are identified and characterized as either perfect separators, which are the global maxima of the proposed optimization scheme or saddle points. The important result emerging from the analysis is that there are no local optima that can prevent the proposed BCA algorithms from converging to perfect separators.
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    Adaptive receiver structures for fiber communication systems employing polarization division multiplexing: high symbol rate case
    (Institute of Electrical and Electronics Engineers (IEEE), 2010) Öktem, Turgut M.; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Demir, Alper; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; 41624; 3756
    Polarization division multiplexing (PDM) has been proposed as a scheme for increasing data rates in fiber optic communication systems. In the PDM scheme, the use of two orthogonal polarizations as alternative data paths is a promising approach in terms of doubling the information rate relative to conventional schemes. However, due to the severe distortion caused by the propagation medium, especially the Polarization mode dispersion (PMD), the development of receiver compensation methods are critical for the deployment of PDM based transceivers. This article proposes a receiver compensation method for high symbol rate fiber optic communication links, where the two data streams sent through orthogonal polarizations are mixed by the fiber channel not only in space but also in time. The proposed receiver algorithm adaptively recovers the original pair of data streams from their space-time mixtures. We also provide simulation results for an end-to-end fiber communication link to illustrate the performance of the proposed approach.
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    Line spectral frequency representation of subbands for speech recognition
    (Elsevier, 1995) Erzin, Engin; N/A; N/A; N/A; N/A
    In this paper, a new set of speech feature parameters is constructed from subband analysis based Line Spectral Frequencies (LSFs). The speech signal is divided into several subbands and the resulting subsignals are represented by LSFs. The performance of the new speech feature parameters, SUBLSFs, is compared with the widely used Mel Scale Cepstral Coefficients (MELCEPs). SUBLSFs are observed to be more robust than the MELCEPs in the presence of car noise. © 1995.