Research Outputs

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    PublicationOpen Access
    Applications of augmented reality in ophthalmology [invited]
    (Optical Society of America (OSA), 2021) Artal, Pablo; Department of Physics; Department of Electrical and Electronics Engineering; Aydındoğan, Güneş; Kavaklı, Koray; Ürey, Hakan; Şahin, Afsun; Faculty Member; Faculty Member; Department of Physics; 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; College of Engineering; School of Medicine; N/A; N/A; 8579; 171267
    Throughout the last decade, augmented reality (AR) head-mounted displays (HMDs) have gradually become a substantial part of modern life, with increasing applications ranging from gaming and driver assistance to medical training. Owing to the tremendous progress in miniaturized displays, cameras, and sensors, HMDs are now used for the diagnosis, treatment, and follow-up of several eye diseases. In this review, we discuss the current state-of-the-art as well as potential uses of AR in ophthalmology. This review includes the following topics: (i) underlying optical technologies, displays and trackers, holography, and adaptive optics; (ii) accommodation, 3D vision, and related problems such as presbyopia, amblyopia, strabismus, and refractive errors; (iii) AR technologies in lens and corneal disorders, in particular cataract and keratoconus; (iv) AR technologies in retinal disorders including age-related macular degeneration (AMD), glaucoma, color blindness, and vision simulators developed for other types of low-vision patients.
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    Publication
    Design and build of small-scale magnetic soft-bodied robots with multimodal locomotion
    (Nature Portfolio, 2023) Ren, Ziyu; Department of Mechanical Engineering; Sitti, Metin; Department of Mechanical Engineering; College of Engineering; School of Medicine
    Small-scale magnetic soft-bodied robots can be designed to operate based on different locomotion modes to navigate and function inside unstructured, confined and varying environments. These soft millirobots may be useful for medical applications where the robots are tasked with moving inside the human body. Here we cover the entire process of developing small-scale magnetic soft-bodied millirobots with multimodal locomotion capability, including robot design, material preparation, robot fabrication, locomotion control and locomotion optimization. We describe in detail the design, fabrication and control of a sheet-shaped soft millirobot with 12 different locomotion modes for traversing different terrains, an ephyra jellyfish-inspired soft millirobot that can manipulate objects in liquids through various swimming modes, a larval zebrafish-inspired soft millirobot that can adjust its body stiffness for efficient propulsion in different swimming speeds and a dual stimuli-responsive sheet-shaped soft millirobot that can switch its locomotion modes automatically by responding to changes in the environmental temperature. The procedure is aimed at users with basic expertise in soft robot development. The procedure requires from a few days to several weeks to complete, depending on the degree of characterization required. The protocol describes a sheet-shaped millirobot with 12 locomotion modes for traversing different terrains, a jellyfish-inspired millirobot for manipulating objects in liquids, a zebrafish-inspired millirobot for efficient swimming and a dual stimuli-responsive millirobot that can switch locomotion modes automatically by responding to the environmental temperature.Rigid-bodied robots lack deformation capabilities, limiting them to specific functions, whereas soft-bodied millibots display sophisticated locomotion strategies similar to those adopted by small-scale organisms. The detailed design and fabrication of small-scale magnetic soft-bodied robots with multimodal locomotion capability, including the processes required for locomotion control and optimization.
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    Publication
    Diffusion-based model for synaptic molecular communication channel
    (IEEE-Inst Electrical Electronics Engineers Inc, 2017) N/A; N/A; N/A; Department of Electrical and Electronics Engineering; Khan, Tooba; Bilgin, Bilgesu Arif; Akan, Özgür Barış; PhD Student; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 6647
    Computational methods have been extensively used to understand the underlying dynamics of molecular communication methods employed by nature. One very effective and popular approach is to utilize a Monte Carlo simulation. Although it is very reliable, this method can have a very high computational cost, which in some cases renders the simulation impractical. Therefore, in this paper, for the special case of an excitatory synaptic molecular communication channel, we present a novel mathematical model for the diffusion and binding of neurotransmitters that takes into account the effects of synaptic geometry in 3-D space and re-absorption of neurotransmitters by the transmitting neuron. Based on this model we develop a fast deterministic algorithm, which calculates expected value of the output of this channel, namely, the amplitude of excitatory postsynaptic potential (EPSP), for given synaptic parameters. We validate our algorithm by a Monte Carlo simulation, which shows total agreement between the results of the two methods. Finally, we utilize our model to quantify the effects of variation in synaptic parameters, such as position of release site, receptor density, size of postsynaptic density, diffusion coefficient, uptake probability, and number of neurotransmitters in a vesicle, on maximum number of bound receptors that directly affect the peak amplitude of EPSP.
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    PublicationOpen Access
    Efficient photocapacitors via ternary hybrid photovoltaic optimization for photostimulation of neurons
    (Optical Society of America (OSA), 2020) Department of Electrical and Electronics Engineering; Srivastava, Shashi Bhushan; Melikov, Rustamzhon; Yıldız, Erdost; Han, Mertcan; Şahin, Afsun; Nizamoğlu, Sedat; Researcher; PhD Student; PhD Student; Master Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; Graduate School of Health Sciences; School of Medicine; College of Engineering; N/A; N/A; N/A; N/A; 171267; 130295
    Optoelectronic photoelectrodes based on capacitive charge-transfer offer an attractive route to develop safe and effective neuromodulators. Here, we demonstrate efficient optoelectronic photoelectrodes that are based on the incorporation of quantum dots (QDs) into poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-Phenyl-C61-butyric acid methyl ester (PCBM) bulk heterojunction. We control the performance of the photoelectrode by the blend ratio, thickness, and nanomorphology of the ternary bulk heterojunction. The optimization led to a photocapacitor that has a photovoltage of 450 mV under a light intensity level of 20 mW.cm(-2) and a responsivity of 99 mA/W corresponding to the most light-sensitive organic photoelectrode reported to date. The photocapacitor can facilitate action potential generation by hippocampal neurons via burst waveforms at an intensity level of 20 mW.cm(-2). Therefore, the results point to an alternative direction in the engineering of safe and ultra-light-sensitive neural interfaces.
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    PublicationOpen Access
    Machine learning-enabled multiplexed microfluidic sensors
    (American Institute of Physics (AIP) Publishing, 2020) Yetişen, Ali Kemal; N/A; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Dabbagh, Sajjad Rahmani; Rabbi, Fazle; Doğan, Zafer; Taşoğlu, Savaş; Faculty Member; Faculty Member; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 280658; 291971
    High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.