Sarper Ozharar is a Senior Researcher in the Optical Sensing & Solutions Department at NEC Laboratories America, where he works on advanced distributed fiber-optic sensing technologies that transform standard communication fibers into highly sensitive instruments capable of detecting vibrations, acoustic signals, and environmental changes. Dr. Ozharar earned his B.S. in Physics from Koç University and later completed his Ph.D. in Optics and Photonics at the University of Central Florida. His academic and research background spans photonics, nonlinear optics, and sensing systems, providing the foundation for his work developing scalable fiber-optic sensing platforms. In recognition of his achievements in science and technology, he received the Achievement Award from Koç University, highlighting the impact of his research and his continued contributions to advancing optical sensing technologies.
His research focuses on distributed acoustic sensing (DAS), which enables fiber-optic cables to serve as large-scale sensor networks for applications such as infrastructure monitoring, transportation systems, environmental observation, and critical infrastructure security. By combining optical physics, signal processing, and system design, his work helps expand the role of fiber networks beyond communications into real-time sensing platforms. He has contributed to projects that demonstrate how fiber-optic sensing can reveal previously hidden patterns in natural and built environments, including innovative experiments showing that underground fiber-optic cables can detect biological and environmental phenomena, such as cicada activity. These efforts illustrate the growing potential of distributed sensing technologies to monitor ecosystems, detect infrastructure disturbances, and provide new sources of environmental data.
Posts
Utility pole detection and localization is the most fundamental application in aerial-optic cables using distributed acoustic sensing (DAS). The existing pole localization method recognizes the hammer knock signal on DAS traces by learning from knocking vibration patterns. However, it requires many efforts for data collection such as knocking every pole and manually labeling the poles’ locations, making this labor-intensive solution expensive, inefficient, and highly error prone. In this paper, we propose a pole localization solution by learning the ambient data collected from a DAS system, which are vibration patterns excited by random ambient events, such as wind and nearby traffic. In detail, we investigate a universal framework for learning representations of ambient data in the frequency domain by contrastive learning of the similarity of low and high-frequency series. A Gaussian-based data reweighting kernel is employed for eliminating the effect of the label noise. Experimental results demonstrate the proposed methods outperform the existing contrastive learning methods on the real-world DAS ambient dataset.
Distributed Fiber Optic Sensing (DFOS) systems rely on measuring and analyzing different properties of the backscattered light of an optical pulse propagating along a fiber cable. DFOS systems can measure temperature, strain, vibrations, or acoustic excitations on the fiber cable and to their unique specifications, they have many applications and advantages over competing technologies. In this talk we will focus on the challenges and applications of DFOS systems using outdoor grade telecom fiber networks instead of standard indoor or some specialty fiber cables.
We review sensing fusion results of integrating fiber sensing with video for machine-learning-based localization and classification of impulsive acoustic event detection. Classification accuracy >97% was achieved on aerial coils, and >99% using fiber-based signal enhancers.
Among the power transmission infrastructures, the low-voltage overhead power lines are specifically critical, due to the complicated roadside environments and the significant number of connections to the end utility users. Maintaining of such a large size grid with mostly wood poles is a challenging task and knowing the operating status and its structural integrity drastically speeds up the routine inspection. Applying a data-driven approach using accelerometer data to analyze the power line-induced vibration to classify different poles within different operational conditions is proposed.Feature creation is the important aspect to improve an accuracy of data-driven algorithms. For this purpose, a time-frequency domain classifier is developed, based on the data collected from two tri-axial accelerometers installed on the wood poles before and after streetlights are on. Data are explored both in time and frequency domain using techniques such as data augmentation and segmentation, averaging, filtering, and principal component analysis. Results of the machine learning classifier clearly shows distinct characteristics among the data collected from different work conditions and different poles. Further exploration of the applied algorithm will be pursued to construct more sophisticated features based on supervised learning to enhance the identification accuracy.
For the first time, we demonstrate detection and classification of rain intensity using Distributed Acoustic Sensing (DAS). An artificial neural network was applied for rain intensity classification and high precision of over 96% was achieved.
We report distributed-fiber-optic-sensing results on impulsive acoustic events localization/classification over telecom networks. A deep-learning-based model was trained to classify starter-gun and fireworks signatures with high accuracy of > 99% using fiber-based-signal-enhancer and >97% using aerial coils.
In this manuscript, we propose and experimentally demonstrate a dispersion-controlled optoelectronic oscillator with phase only modulator at 18 GHz. The generated microwave signal has a phase noise of −108 dBc/Hz at 10 kHz offset frequency and the integrated timing jitter is calculated to be 16.2 fs (1 kHz to 100 MHz) and 20 fs (1kHz to Nyquist).
For the first time to our knowledge, a stationary weight hanging on an operational aerial telecommunication field fiber was detected and localized using only ambient data collected by a φ-DAS system. Although stationary weights do not create temporally varying signals, and hence cannot be observed directly from the DAS traces, the existence and the location of the additional weights were revealed by the operational modal analysis of the aerial fiber structure.
We demonstrated for the first time to our knowledge, the detection and localization of a static weight on an aerial cable by using frequency domain decomposition analysis of ambient vibrations detected by a φ-DAS system.
In distributed acoustic sensing (DAS) on aerial fiber-optic cables, utility pole localization is a prerequisite for any subsequent event detection. Currently, localizing the utility poles on DAS traces relies on human experts who manually label the poles’ locations by examining DAS signal patterns generated in response to hammer knocks on the poles. This process is inefficient, error-prone and expensive, thus impractical and non-scalable for industrial applications. In this paper, we propose two machine learning approaches to automate this procedure for large-scale implementation. In particular, we investigate both unsupervised and supervised methods for fine-grained pole localization. Our methods are tested on two real-world datasets from field trials, and demonstrate successful estimation of pole locations at the same level of accuracy as human experts and strong robustness to label noises.
