Research Outputs
Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
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Publication Metadata only CNN-based page segmentation and object classification for counting population in ottoman archival documentation(Multidisciplinary Digital Publishing Institute (MPDI), 2020) Department of History; N/A; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Researcher; Department of History; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 33267; N/AHistorical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.Publication Metadata only Flexible luma-chroma bit allocation in learned image compression for high-fidelity sharper images(IEEE, 2022) N/A; Department of Electrical and Electronics Engineering; Ulaş, Ökkeş Uğur; Tekalp, Ahmet Murat; Master student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 26207High-fidelity learned image/video compression solutions are typically optimized with respect to l1 or l2 loss in RGB 444 format and evaluated by RGB PSNR. It is well-known that optimization of a fidelity criterion results in blurry images, which is typically alleviated by adding a content-based and/or adversarial loss terms. However, such conditional generative models result in loss of fidelity. In this paper, we propose a simple solution to obtain sharper images without losing fidelity based on learned flexible-rate coding using gained variational auto-encoder (gained-VAE) in the luma-chroma (YCrCb 444) domain. This allows us to implement image-adaptive luma-chroma bit allocation during inference, i.e., to increase Y PSNR at the expense of slightly lower chroma PSNR to obtain sharper images without introducing color artifacts based on the observation that Y PSNR correlates with image sharpness better than RGB PSNR. We note that the proposed inference-time image-adaptive luma-chroma bit allocation strategy can be incorporated into any VAE-based image compression model. Experimental results show that sharper images with better VMAF and Y PSNR can be obtained by optimizing models for YCrCb MSE with the proposed image-adaptive luma-chroma bit/quality allocation compared to stateof-the-art models optimizing RGB MSE at the same bpp.Publication Metadata only Flexible-rate learned hierarchical bi-directional video compression with motion refinement and frame-level bit allocation(IEEE Computer Society, 2022) Department of Electrical and Electronics Engineering; N/A; N/A; Tekalp, Ahmet Murat; Yılmaz, Mustafa Akın; Çetin, Eren; Faculty Member; PhD Student; Undergraduate Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 26207; N/A; N/AThis paper presents improvements and novel additions to our recent work on end-to-end optimized hierarchical bidirectional video compression [1] to further advance the state-of-the-art in learned video compression. As an improvement, we combine motion estimation and prediction modules and compress refined residual motion vectors for improved rate-distortion performance. As novel addition, we adapted the gain unit proposed for image compression to flexible-rate video compression in two ways: first, the gain unit enables a single encoder model to operate at multiple rate-distortion operating points; second, we exploit the gain unit to control bit allocation among intra-coded vs. bi-directionally coded frames by fine tuning corresponding models for truly flexible-rate learned video coding. Experimental results demonstrate that we obtain state-of-the-art rate-distortion performance exceeding those of all prior art in learned video coding.Publication Metadata only Low-PAPR waveforms with shaped spectrum for enhanced low probability of intercept noise radars(Mdpi, 2021) Galati, Gaspare; Pavan, Gabriele; N/A; Savcı, Kubilay; PhD Student; Graduate School of Sciences and Engineering; N/ANoise radars employ random waveforms in their transmission as compared to traditional radars. Considered as enhanced Low Probability of Intercept (LPI) radars, they are resilient to interference and jamming and less vulnerable to adversarial exploitation than conventional radars. At its simplest, using a random waveform such as bandpass Gaussian noise as a probing signal provides limited radar performance. After a concise review of a particular noise radar architecture and related correlation processing, this paper justifies the rationale for having synthetic (tailored) noise waveforms and proposes the Combined Spectral Shaping and Peak-to-Average Power Reduction (COSPAR) algorithm, which can be utilized for synthesizing noise-like sequences with a Taylor-shaped spectrum under correlation sidelobe level constraints and assigned Peak-to-Average-Power-Ratio (PAPR). Additionally, the Spectral Kurtosis measure is proposed to evaluate the LPI property of waveforms, and experimental results from field trials are reported.Publication Metadata only Optoacoustic tracking and magnetic manipulation of cell-sized microrobots in mice(Optica Publishing Group, 2022) Wrede, Paul; Degtyaruk, Oleksiy; Kalva, Sandeep Kumar; Luis Deán-Ben, Xosé; Bozuyuk, Uğur; Aghakhani, Amirreza; Akolpoglu, Birgul; Razansky, Daniel; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; College of Engineering; 297104We propose noninvasive real-time 3D tracking of circulating cell-sized magnetic microrobots with optoacoustic tomography. The approach paves the way for effective and safe intravascular operation of microrobots in clinically-relevant environments.