Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/47

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    Article
    Citation - WoS: 27
    Citation - Scopus: 45
    Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of Covid-19 Outbreak in Italy
    (Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
    Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.
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    Citation - WoS: 38
    Citation - Scopus: 44
    Physical-Layer Security With Optical Generalized Space Shift Keying
    (Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020) Panayırcı, Erdal; Yeşilkaya, Anıl; Çoğalan, Tezcan; Poor, H. Vincent; Haas, Harald
    Spatial modulation (SM) is a promising technique that reduces inter-channel interference while providing high power efficiency and detection simplicity. In order to ensure the secrecy of SM, precoding and friendly jamming are widely adopted in the literature. However, neither of those methods can take advantage of SM. In this paper, a novel spatial constellation design (SCD) technique is proposed to enhance the physical layer security (PLS) of optical generalized space shift keying (GSSK), which can retain some benefits of SM. Due to the lack of small-scale fading, the quasi-static characteristics of the optical channel is used to tailor the received signal at the legitimate user's (Bob's) side. The PLS of the system is guaranteed by the appropriate selection of the power allocation coefficients for randomly activated light emitting diodes (LEDs). With the aid of Bob's channel state information at the transmitter, the bit error ratio (BER) of Bob is minimized while the BER performance of the potential eavesdroppers (Eves) is significantly degraded. Monte-Carlo simulation results show that the proposed SCD-zero forcing precoding (ZFP) forces Eve to experience a BER of around 0.5 by outperforming both the conventional and ZFP based GSSK for all practical signal-to-noise-ratio regimes and Bob-Eve separations.