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Publication Metadata only Audio-driven human body motion analysis and synthesis(IEEE, 2008) Canton-Ferrer, C.; Tilmanne, J.; Bozkurt, E.; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Ofli, Ferda; Demir, Yasemin; Yemez, Yücel; Erzin, Engin; Tekalp, Ahmet Murat; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 107907; 34503; 26207This paper presents a framework for audio-driven human body motion analysis and synthesis. We address the problem in the context of a dance performance, where gestures and movements of the dancer are mainly driven by a musical piece and characterized by the repetition of a set of dance figures. The system is trained in a supervised manner using the multiview video recordings of the dancer. The human body posture is extracted from multiview video information without any human intervention using a novel marker-based algorithm based on annealing particle filtering. Audio is analyzed to extract beat and tempo information. The joint analysis of audio and motion features provides a correlation model that is then used to animate a dancing avatar when driven with any musical piece of the same genre. Results are provided showing the effectiveness of the proposed algorithm.Publication Metadata only Clinical validation of SERS metasurface SARS-CoV-2 biosensor(Spie-Int Soc Optical Engineering, 2022) İlgu, Müslüm; Yanık, Cenk; Çelik, Süleyman; Öztürk, Meriç; N/A; N/A; Department of Electrical and Electronics Engineering; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Bilgin, Buse; Torun, Hülya; Doğan, Özlem; Ergönül, Önder; Solaroğlu, İhsan; Can, Füsun; Onbaşlı, Mehmet Cengiz; PhD Student; PhD Student; Undergraduated Student; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; Koç University Hospital; N/A; N/A; N/A; 170418; 110398; 102059; 103165; 258783The real-time polymerase chain reaction (RT-PCR) analysis using nasal swab samples is the gold standard approach for COVID-19 diagnosis. However, due to the high false-negative rate at lower viral loads and complex test procedure, PCR is not suitable for fast mass screening. Therefore, the need for a highly sensitive and rapid detection system based on easily collected fluids such as saliva during the pandemic has emerged. In this study, we present a surface-enhanced Raman spectroscopy (SERS) metasurface optimized with genetic algorithm (GA) to detect SARS-CoV-2 directly using unprocessed saliva samples. During the GA optimization, the electromagnetic field profiles were used to calculate the field enhancement of each structure and the fitness values to determine the performance of the generated substrates. The obtained design was fabricated using electron beam lithography, and the simulation results were compared with the test results using methylene blue fluorescence dye. After the performance of the system was validated, the SERS substrate was tested with inactivated SARS-CoV-2 virus for virus detection, viral load analysis, cross-reactivity, and variant detection using machine learning models. After the inactivated virus tests are completed, with 36 PCR positive and 33 negative clinical samples, we were able to detect the SARS-CoV-2 positive samples from Raman spectra with 95.2% sensitivity and specificity.Publication Metadata only Importance of vesicle release stochasticity in neuro-spike communication(Institute of Electrical and Electronics Engineers (IEEE), 2017) Akan, Ozgur B.; N/A; Department of Electrical and Electronics Engineering; Ramezani, Hamideh; PhD Student; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/AAim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.Publication Metadata only Investigating the effect of body composition differences on seismocardiogram characteristics(IEEE Computer Soc, 2023) Tokmak, Fadime; Department of Electrical and Electronics Engineering; Gürsoy, Beren Semiz; Department of Electrical and Electronics Engineering; College of EngineeringIn seismocardiogram (SCG) analysis, inter-subject variability is observed as the medium between the heart and accelerometer consists of different tissues made of bone, muscle, fat and skin cells of which combination varies across different people. Anatomically, a similar pattern is present in the speech production system, where the vocal cord and vocal tract are considered as the source and medium, respectively. For observing the change of the vocal tract filter while voicing different sounds, linear predictive analysis has been used for years. Thus, it was hypothesized that the medium characteristics of the human thorax would also have a filtering effect on the SCG signals and the differences in the filtering effects would be observed in the respiration (<1 Hz), vibration (1-20 Hz) and acoustic (>20 Hz) characteristics of the SCG signals. To that aim, three different binary classification tasks representing the body composition differences were defined: (i) whether the metabolic age of the subject is more than the real age of the subject, (ii) whether the BMI of the subject is bigger than 25, and (iii) whether the subject is male or female. To understand the metabolism-induced changes in the respiration, vibration and acoustic components, classification experiments were conducted using different frequency bands of the SCG signal. In each case, linear predictive coefficients were extracted and used to train individual classification models for the aforementioned scenarios. With the vibration components (120 Hz), all of the tasks resulted in high performance (0.86, 0.93, 0.93) for age, BMI and gender classification tasks, respectively. This study reveals that the vibration components of SCG make a stable and informative contribution to selected classification tasks, and due to its high generalizability, it is suitable for various practical applications.Publication Metadata only Machine learning-based approach to identify formalin-fixed paraffin-embedded glioblastoma and healthy brain tissues(Spie-Int Soc Optical Engineering, 2022) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Torun, Hülya; Batur, Numan; Bilgin, Buse; Esengür, Ömer Tarık; Baysal, Kemal; Kulaç, İbrahim; Solaroğlu, İhsan; Onbaşlı, Mehmet Cengiz; PhD Student; Undergraduate Student; PhD Student; Undergraduate Student; Faculty Member; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; School of Medicine; School of Medicine; School of Medicine; School of Medicine; College of Engineering; N/A; N/A; N/A; N/A; 119184; 170305; 102059; 258783Glioblastoma is the most malignant and common high-grade brain tumor with a 14-month overall survival length. According to recent World Health Organization Central Nervous System tumor classification (2021), the diagnosis of glioblastoma requires extensive molecular genetic tests in addition to the traditional histopathological analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Time-consuming and expensive molecular tests as well as the need for clinical neuropathology expertise are the challenges in the diagnosis of glioblastoma. Hence, an automated and rapid analytical detection technique for identifying brain tumors from healthy tissues is needed to aid pathologists in achieving an error-free diagnosis of glioblastoma in clinics. Here, we report on our clinical test results of Raman spectroscopy and machine learning-based glioblastoma identification methodology for a cohort of 20 glioblastoma and 18 white matter tissue samples. We used Raman spectroscopy to distinguish FFPE glioblastoma and white matter tissues applying our previously reported protocols about optimized FFPE sample preparation and Raman measurement parameters. One may analyze the composition and identify the subtype of brain tumors using Raman spectroscopy since this technique yields detailed molecule-specific information from tissues. We measured and classified the Raman spectra of neoplastic and non-neoplastic tissue sections using machine learning classifiers including support vector machine and random forest with 86.6% and 83.3% accuracies, respectively. These proof-of-concept results demonstrate that this technique might be eventually used in the clinics to assist pathologists once validated with a larger and more diverse glioblastoma cohort and improved detection accuracies.Publication Open Access Microsphere-based optical system for biosensor applications(Society of Photo-optical Instrumentation Engineers (SPIE), 2004) Department of Physics; Department of Electrical and Electronics Engineering; İşçi, Şenol; Bilici, Temel; Serpengüzel, Ali; Kurt, Adnan; Faculty Member; Teaching Faculty; Department of Physics; Department of Electrical and Electronics Engineering; College of Sciences; N/A; N/A; 27855; 194455Optical microsphere resonators have been recently utilized in quantum optics, laser science, spectroscopy, and optoelectronics and attracted increasing interest due to their unique optical properties. Microspheres possess high quality factor (Q-factor) optical morphology dependent resonances, and have relatively small volumes. High-Q morphology dependent resonances are very sensitive to the refractive index change and microsphere uniformity. These tiny optical cavities, whose diameters may vary from a few to several hundred micrometers, have resonances with reported Q-factors as large as 3 x 10(9). Due to their sensitivity, morphology dependent resonances of microspheres are also considered for biosensor applications. Binding of a protein or other biomolecules can be monitored by observing the wavelength shift of morphology dependent resonances. A biosensor, based on this optical phenomenon, can even detect a single molecule, depending on the quality of the system design. In this work, elastic scattering spectra from the microspheres of different materials are experimentally obtained and morphology dependent resonances are observed. Preliminary results of unspecific binding of biomolecules onto the microspheres are presented. Furthermore, the morphology dependent resonances of the microspheres for biosensor applications are analyzed theoretically both for proteins such as bovine serum albumin.Publication Metadata only Noise analysis problems and techniques for RF electronic circuits and optical fiber communication systems(IEEE, 2007) Department of Electrical and Electronics Engineering; Demir, Alper; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 3756Various forms of linearized noise analysis techniques have been extensively used for analyzing the performance of electronic circuits in the presence of undesired disturbances since the early 70's. Practical numerical algorithms based on linearized perturbation formulations have been implemented in public domain and commercial electronic circuit simulators. In this paper, we describe the noise analysis problems in optical fiber communications and discuss their relationships to RF circuit noise analysis. In particular, we recognize that the noise analysis of electronic circuits with time-invariant (time-varying) large signal excitations corresponds to the analysis of noise propagation in optical fibers along with unmodulated (modulated) light carriers. We discuss how linearized perturbation formulations are used for noise analysis in both domains and draw analogies between them by providing a comparative review of the techniques that have been proposed in the literature.Publication Metadata only Noise in neuronal and electronic circuits: a general modeling framework and non-monte carlo simulation techniques(Ieee-Inst Electrical Electronics Engineers Inc, 2017) N/A; N/A; Department of Electrical and Electronics Engineering; Kılınç, Deniz; Demir, Alper; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 3756The brain is extremely energy efficient and remarkably robust in what it does despite the considerable variability and noise caused by the stochastic mechanisms in neurons and synapses. Computational modeling is a powerful tool that can help us gain insight into this important aspect of brain mechanism. A deep understanding and computational design tools can help develop robust neuromorphic electronic circuits and hybrid neuroelectronic systems. In this paper, we present a general modeling framework for biological neuronal circuits that systematically captures the nonstationary stochastic behavior of ion channels and synaptic processes. In this framework, fine-grained, discrete-state, continuous-time Markov chain models of both ion channels and synaptic processes are treated in a unified manner. Our modeling framework features a mechanism for the automatic generation of the corresponding coarse-grained, continuous-state, continuous-time stochastic differential equation models for neuronal variability and noise. Furthermore, we repurpose non-Monte Carlo noise analysis techniques, which were previously developed for analog electronic circuits, for the stochastic characterization of neuronal circuits both in time and frequency domain. We verify that the fast non-Monte Carlo analysis methods produce results with the same accuracy as computationally expensive Monte Carlo simulations. We have implemented the proposed techniques in a prototype simulator, where both biological neuronal and analog electronic circuits can be simulated together in a coupled manner.Publication Metadata only On the convergence of ICA algorithms with symmetric orthogonalization(IEEE, 2008) Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 41624We study the convergence behavior of Independent Component Analysis (ICA) algorithms that are based on the contrast function maximization and that employ symmetric orthogonalization method to guarantee the orthogonality property of the search matrix. In particular, the characterization of the critical points of the corresponding optimization problem and the stationary points of the conventional gradient ascent and fixed point algorithms are obtained. As an interesting and a useful feature of the symmetrical orthogonalization method, we show that the use of symmetric orthogonalization enables the monotonic convergence for the fixed point ICA algorithms that are based on the convex contrast functions.Publication Open Access Oscillator noise analysis(American Institute of Physics (AIP) Publishing, 2005) Department of Electrical and Electronics Engineering; Demir, Alper; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 3756Oscillators are key components of many kinds of systems, particularly electronic and opto-electronic systems. Undesired perturbations, i.e. noise, that exist in practical systems adversely affect the spectral and timing properties of the signals generated by oscillators resulting in phase noise and timing jitter. These are key performance limiting factors, being major contributors to bit-error-rate (BER) of RF and optical communication systems, and creating synchronization problems in clocked and sampled-data electronic systems. In noise analysis for oscillators, the key is figuring out how the various disturbances and noise sources in the oscillator end up as phase fluctuations. In doing so, one first computes transfer functions from the noise sources to the oscillator phase, or the sensitivity of the oscillator phase to these noise sources. In this paper, we first provide a discussion explaining the origins and the proper definition of this transfer or sensitivity function, followed by a critical review of the various numerical techniques for its computation that have been proposed by various authors over the past fifteen years.