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Publication Open Access 3D-printed multi-stimuli-responsive mobile micromachines(American Chemical Society (ACS), 2020) Lee, Yun-Woo; Ceylan, Hakan; Yasa, İmmihan Ceren; Department of Mechanical Engineering; Kılıç, Uğur; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of EngineeringMagnetically actuated and controlled mobile micromachines have the potential to be a key enabler for various wireless lab-on-a-chip manipulations and minimally invasive targeted therapies. However, their embodied, or physical, task execution capabilities that rely on magnetic programming and control alone can curtail their projected performance and functional diversity. Integration of stimuli-responsive materials with mobile magnetic micromachines can enhance their design toolbox, enabling independently controlled new functional capabilities to be defined. To this end, here, we show three-dimensional (3D) printed size-controllable hydrogel magnetic microscrews and microrollers that respond to changes in magnetic fields, temperature, pH, and divalent cations. We show two-way size-controllable microscrews that can reversibly swell and shrink with temperature, pH, and divalent cations for multiple cycles. We present the spatial adaptation of these microrollers for penetration through narrow channels and their potential for controlled occlusion of small capillaries (30 μm diameter). We further demonstrate one-way size-controllable microscrews that can swell with temperature up to 65% of their initial length. These hydrogel microscrews, once swollen, however, can only be degraded enzymatically for removal. Our results can inspire future applications of 3D- and 4D-printed multifunctional mobile microrobots for precisely targeted obstructive interventions (e.g., embolization) and lab- and organ-on-a-chip manipulations.
Publication Metadata only A class of bounded component analysis algorithms for the separation of both independent and dependent sources(IEEE-inst Electrical Electronics Engineers inc, 2013) Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 41624Bounded Component analysis (BCa) is a recent approach which enables the separation of both dependent and independent signals from their mixtures. in this approach, under the practical source boundedness assumption, the widely used statistical independence assumption is replaced by a more generic domain separability assumption. This article introduces a geometric framework for the development of Bounded Component analysis algorithms. Two main geometric objects related to the separator output samples, namely Principal Hyper-Ellipsoid and Bounding Hyper-Rectangle, Are introduced. the maximization of the volume ratio of these objects, and its extensions, Are introduced as relevant optimization problems for Bounded Component analysis. the article also provides corresponding iterative algorithms for both real and complex sources. the numerical examples illustrate the potential advantage of the proposed BCa framework in terms of correlated source separation capability as well as performance improvement for short data records.Publication Metadata only A communication theoretical analysis of synaptic multiple-access channel in hippocampal-cortical neurons(IEEE-Inst Electrical Electronics Engineers Inc, 2013) N/A; N/A; Department of Electrical and Electronics Engineering; Malak, Derya; Akan, Özgür Barış; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6647Communication between neurons occurs via transmission of neural spike trains through junctional structures, either electrical or chemical synapses, providing connections among nerve terminals. Since neural communication is achieved at synapses, the process of neurotransmission is called synaptic communication. Learning and memory processes are based on the changes in strength and connectivity of neural networks which usually contain multiple synaptic connections. In this paper, we investigate multiple-access neuro-spike communication channel, in which the neural signal, i.e., the action potential, is transmitted through multiple synaptic paths directed to a common postsynaptic neuron terminal. Synaptic transmission is initiated with random vesicle release process from presynaptic neurons to synaptic paths. Each synaptic channel is characterized by its impulse response and the number of available postsynaptic receptors. Here, we model the multiple-access synaptic communication channel, and investigate the information rate per spike at the postsynaptic neuron, and how postsynaptic rate is enhanced compared to single terminal synaptic communication channel. Furthermore, we analyze the synaptic transmission performance by incorporating the role of correlation among presynaptic terminals, and point out the postsynaptic rate improvement.Publication Metadata only A comparison of stochastic and interval finite elements applied to shear frames with uncertain stiffness properties(Elsevier, 1998) Elishakoff, I; Department of Mathematics; Köylüoğlu, Hasan Uğur; Teaching Faculty; Department of Mathematics; College of Sciences; N/AStructural uncertainties are modelled using stochastic and interval methods to quantify the uncertainties in the response quantities. Through a suitable discretization, stochastic and interval finite element methods are constructed. A comparison of these methods is illustrated using a shear frame with uncertain stiffness properties.Publication Open Access A computational multicriteria optimization approach to controller design for pysical human-robot interaction(Institute of Electrical and Electronics Engineers (IEEE), 2020) Tokatlı, Ozan; Patoğlu, Volkan; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 125489Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.Publication Metadata only A convolutive bounded component analysis framework for potentially nonstationary independent and/or dependent sources(IEEE-Inst Electrical Electronics Engineers Inc, 2015) İnan, Hüseyin A.; Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 41624Bounded Component Analysis (BCA) is a recent framework which enables development of methods for the separation of dependent as well as independent sources from their mixtures. This paper extends a recent geometric BCA approach introduced for the instantaneous mixing problem to the convolutive mixing problem. The paper proposes novel deterministic convolutive BCA frameworks for the blind source extraction and blind source separation of convolutive mixtures of sources which allows the sources to be potentially nonstationary. The global maximizers of the proposed deterministic BCA optimization settings are proved to be perfect separators. The paper also illustrates that the iterative algorithms corresponding to these frameworks are capable of extracting/separating convolutive mixtures of not only independent sources but also dependent (even correlated) sources in both component (space) and sample (time) dimensions through simulations based on a Copula distributed source system. In addition, even when the sources are independent, it is shown that the proposed BCA approach have the potential to provide improvement in separation performance especially for short data records based on the setups involving convolutive mixtures of digital communication sources.Publication Metadata only A fast blind equalization method based on subgradient projections(IEEE, 2004) N/A; N/A; Department of Electrical and Electronics Engineering; Kızılkale, Can; Erdoğan, Alper Tunga; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624A novel blind equalization method based on a subgradient search over a convex cost surface is proposed. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA) which mostly suffer from the convergence problems caused by their non-convex cost functions. The proposed method is an iterative algorithm, for both real and complex constellations, with a very simple update rule that minimizes the l(infinity) norm of the equalizer output under a linear constraint on the equalizer coefficients. The algorithm has a nice convergence behavior attributed to the convex l(infinity) cost surface. Examples are provided to illustrate the algorithm's performance.Publication Open Access A fast, accurate, and separable method for fitting a Gaussian function(Institute of Electrical and Electronics Engineers (IEEE), 2019) Al-Nahhal Ibrahim; Dobre Octavia A.; Moloney Cecilia; Ikki Salama; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 149116Publication Open Access A machine learning approach for implementing data-driven production control policies(Taylor _ Francis, 2021) Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AGiven the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.Publication Open Access A method for estimating stock-out-based substitution rates by using point-of-sale data(Taylor _ Francis, 2009) Öztürk, Ömer Cem; Department of Business Administration; Tan, Barış; Karabatı, Selçuk; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600; 38819Empirical studies in retailing suggest that stock-out rates are quite high in many product categories. Stock-outs result in demand spillover, or substitution, among items within a product category. Product assortment and inventory management decisions can be improved when the substitution rates are known. In this paper, a method is presented to estimate product substitution rates by using only Point-Of-Sale (POS) data. The approach clusters POS intervals into states where each state corresponds to a specific substitution scenario. Then available POS data for each state is consolidated and the substitution rates are estimated using the consolidated information. An extensive computational analysis of the proposed substitution rate estimation method is provided. The computational analysis and comparisons with an estimation method from the literature show that the proposed estimation method performs satisfactorily with limited information.