Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Patent Artificial Intelligence Augmented Iterative Product Decoding(2023) Arslan , Şuayb Şefik; Göker, TurguyA method for product decoding within a data storage system includes receiving data to be decoded within a first decoder; performing a plurality of decoding iterations to decode the data utilizing a first decoder and a second decoder; and outputting fully decoded data based on the performance of the plurality of decoding iterations. Each of the plurality of decoding iterations includes (i) decoding the data with the first decoder operating at a first decoder operational mode to generate once decoded data; (ii) sending the once decoded data from the first decoder to the second decoder; (iii) receiving error information from the first decoder with an artificial intelligence system; (iv) selecting a second decoder operational mode based at least in part on the error information that is received by the artificial intelligence system; and (v) decoding the once decoded data with the second decoder operating at the second decoder operational mode to generate twice decoded data; and outputting fully decoded data based on the performance of the plurality of decoding iterations.Patent Joint Multi-Nanopore Sequencing for Reliable Data Retrieval in Nucleic Acid Storage(2023) Arslan , Şuayb Şefik; Göker, Turguy; Doerner, DonA nucleic acid storage system (100) that uses nanopore sequencing to read data values chemically embedded in oligonucleotides includes a membrane (102), a voltage source (108), and a nucleic acid strand (110). The membrane (102) has a plurality of nanopores (104) that are stacked upon one another in a multi-nanopore arrangement. The voltage source (108) is configured to direct voltage across the plurality of nanopores (104). The nucleic acid strand (110) including the oligonucleotides is threaded through each of the plurality of nanopores (104) within the membrane (102). A separate base signal (118) is generated from the nucleic acid strand (110) being threaded through each of the plurality of nanopores (104), and Recursive Neural Networks can be used to estimate a signal shape for each oligonucleotide. Recurrent Convolutional Neural Networks and noise predictive data detection algorithms can be used based on the estimated signal shapes to sequence the oligonucleotides.Article What Is the Effective Resolution of the Retinal Image of a Distant Face?(Vision Sciences Society Annual Meeting Abstract, 2023) Arslan , Şuayb Şefik; Arslan, Şefik Şuayb; Sinha, Pawan; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityWe consider the following question: What is the effective resolution of a face image projected on the retina, when the face is at a specified distance from the eye? Though simple to state, this is a surprisingly challenging issue to resolve. The mapping between viewing distance and effective resolution cannot be readily derived based on the contrast sensitivity, Snellen acuity, or even the packing density of photoreceptors in the fovea. With initial guidelines derived from theoretical considerations, images of varying resolution were presented across a range of viewing distances. For each distance, participants were required to perform an ‘odd one out’ task. This involved detecting the one that was different from the rest in a 2x2 grid, with image resolution being the only dimension of variation. As the experiment progressed, the viewing distance decreased monotonically, and participants were able to detect increasingly subtle resolution differences between the three standard images and the outlier. The collected data have allowed us to establish the upper/lower bounds on the effective available resolution for typical human vision as a function of viewing distance. Interestingly, we find that humans perform significantly better, particularly at short ranges, than what a theoretical model predicts based on projected image size, cone density, and foveal extent. Accordingly, we suggest that the non-uniform in-fovea density, as well as less sharp fall-off in the acuity density function outside the fovea, need to be integrated into future theoretical models to translate viewing distance to perceived image characteristics. A pragmatic benefit of the <distance : effective-resolution> mapping is that it enables a direct comparison of human face recognition performance as assessed across blur and viewing distance. Additionally, it allows us to systematically compare human performance on face recognition at varying distances with that of machine vision systems using the common axis of resolution.Article Comparing Humans and Deep Neural Networks on Face Recognition Under Various Distance and Rotation Viewing Conditions(Journal of Vision, 2023) Fux, Michal; Arslan, Şefik Şuayb; Jang, Hojin; Boix, Xavier; Cooper, Avi; Groth, Matt J; Sinha, Pawan; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityHumans possess impressive skills for recognizing faces even when the viewing conditions are challenging, such as long ranges, non-frontal regard, variable lighting, and atmospheric turbulence. We sought to characterize the effects of such viewing conditions on the face recognition performance of humans, and compared the results to those of DNNs. In an online verification task study, we used a 100 identity face database, with images captured at five different distances (2m, 5m, 300m, 650m and 1000m) three pitch values (00 - straight ahead, +/- 30 degrees) and three levels of yaw (00, 45, and 90 degrees). Participants were presented with 175 trials (5 distances x 7 yaw and pitch combinations, with 5 repetitions). Each trial included a query image, from a certain combination of range x yaw x pitch, and five options, all frontal short range (2m) faces. One was of the same identity as the query, and the rest were the most similar identities, chosen according to a DNN-derived similarity matrix. Participants ranked the top three most similar target images to the query image. The collected data reveal the functional relationship between human performance and multiple viewing parameters. Nine state-of-the-art pre-trained DNNs were tested for their face recognition performance on precisely the same stimulus set. Strikingly, DNN performance was significantly diminished by variations in ranges and rotated viewpoints. Even the best-performing network reported below 65% accuracy at the closest distance with a profile view of faces, with results dropping to near chance for longer ranges. The confusion matrices of DNNs were generally consistent across the networks, indicating systematic errors induced by viewing parameters. Taken together, these data not only help characterize human performance as a function of key ecologically important viewing parameters, but also enable a direct comparison of humans and DNNs in this parameter regime