Publications

@article{Topp11072024}, author = {Sven Topp, Shuangshuang Xiao, Basil Duvernoy, Jeraldine Milroy, Zhanat Kappassov, Nurlan Kabdyshev, Roope Raisamo, Vincent Hayward and Mounia Ziat}, title = {Mediated and non-mediated tactile fingerspelling: a comparative study}, journal = {Assistive Technology}, volume = {0}, number = {0}, pages = {1--10}, year = {2024}, publisher = {Taylor \& Francis}, doi = {10.1080/10400435.2024.2369547}, note ={PMID: 38990181}, URL = { https://doi.org/10.1080/10400435.2024.2369547 }, eprint = { https://doi.org/10.1080/10400435.2024.2369547 } }

@ARTICLE{10751786, author={Chibar, Rustam and Kostyukova, Valeriya and Khajikhanov, Soibkhon and Kenzhebek, Daryn and Zhakatayev, Altay and Orazbayev, Bakhtiyar and Kappassov, Zhanat}, journal={IEEE Sensors Journal}, title={Honeycomb-Inspired Metamaterial for Tactile Sensors with Variable Stiffness}, year={2024}, volume={}, number={}, pages={1-1}, keywords={Sensors;Robot sensing systems;Collision avoidance;Robots;Metamaterials;Tactile sensors;Dynamics;Honeycomb structures;Force;Magnetic materials;Force and tactile sensing;collision detection;compliance and impedance control;variable stiffness;tactile sensors;physical interactions;nonlinear stiffness;honeycombs;potential energy storage}, doi={10.1109/JSEN.2024.3492498}}

@ARTICLE{10758224, author={Mukashev, Dinmukhammed and Seitzhan, Saltanat and Chumakov, Jabrail and Khajikhanov, Soibkhon and Yergibay, Madina and Zhaniyar, Nurlan and Chibar, Rustam and Mazhitov, Ayan and Rubagotti, Matteo and Kappassov, Zhanat}, journal={IEEE Transactions on Robotics}, title={E-BTS: Event-Based Tactile Sensor for Haptic Teleoperation in Augmented Reality}, year={2025}, volume={41}, number={}, pages={450-463}, keywords={Robot sensing systems;Robots;Tactile sensors;Haptic interfaces;Force;Event detection;Cameras;Visualization;Robot vision systems;Three-dimensional printing;Augmented reality (AR);event-based vision;haptics;remote sensing;tactile sensors;visual force feedback (VFF)}, doi={10.1109/TRO.2024.3502215}}

@ARTICLE{10649586, author={Zholtayev, Darkhan and Dauletiya, Daniyar and Tileukulova, Aisulu and Akimbay, Dias and Nursultan, Manat and Bushanov, Yersaiyn and Kuzdeuov, Askat and Yeshmukhametov, Azamat}, journal={IEEE Access}, title={Smart Pipe Inspection Robot With In-Chassis Motor Actuation Design and Integrated AI-Powered Defect Detection System}, year={2024}, volume={12}, number={}, pages={119520-119534}, keywords={Inspection;Robots;Image edge detection;Simultaneous localization and mapping;Computational modeling;Sensors;Machine learning;Inpipe robot;robot design;machine learning;defect detection;pipe inspection;SLAM}, doi={10.1109/ACCESS.2024.3450502}}

@article{AWAN2024104706, title = {A GIS-Based emergency response and management support framework for Earthquake crisis: A case study of Antakya and Kahramanmaras earthquake in Turkey}, journal = {International Journal of Disaster Risk Reduction}, volume = {111}, pages = {104706}, year = {2024}, issn = {2212-4209}, doi = {https://doi.org/10.1016/j.ijdrr.2024.104706}, url = {https://www.sciencedirect.com/science/article/pii/S2212420924004680}, author = {Hamad Hassan Awan and Marzhan Kabdrakhmanova and Huseyin Atakan Varol and Ferhat Karaca}, keywords = {Resilience, GIS, Emergency response framework, Satellite imagery}, abstract = {Earthquakes can cause significant damage, including loss of lives, collapsed buildings, and blocked roads. However, the severity and frequency of earthquakes cannot be accurately predicted. To mitigate these effects, successful disaster management, particularly in improving post-earthquake road network connectivity, is crucial. Therefore, this study presents a novel satellite imagery-based approach for identifying post-earthquake road blockage and to assess its impact on urban infrastructure and emergency response management. This study utilised real-world pre- and post-earthquake satellite images of the earthquake that struck Turkey on February 6, 2023 using ArcGIS Pro. The results demonstrated that in the scenario when people in affected areas (incidents) tried to escape to emergency gathering zones, out of the initial 126 incident locations in Antakya, 101 of them experienced alterations in the selected routes after the earthquake. In Kahramanmaras, out of 35 incidents, 28 experienced changes in the selected routes. In the case of emergency vehicles (firefighting, medical aid, etc.) traveling to affected areas, some emergency vehicles were assigned a great number of incidents, whereas some vehicles had no incidents assigned to them. The findings of this study suggest that the developed framework can identify road blockage and inefficiencies in an emergency response framework with high accuracy. Therefore, the approach presented in this paper can be implemented to develop a practical geographic information system (GIS) based model to enhance the emergency response management for potential future earthquakes. Thereby, this study can facilitate the level of resilience of urban environments to withstand and adapt to future crises.} }

@ARTICLE{10374245, author={Abdrakhmanova, Madina and Unaspekov, Timur and Varol, Huseyin Atakan}, journal={IEEE Transactions on Biometrics, Behavior, and Identity Science}, title={Multimodal Person Verification With Generative Thermal Data Augmentation}, year={2023}, volume={}, number={}, pages={1-1}, doi={10.1109/TBIOM.2023.3346938}}

@Article{nu15071728, AUTHOR = {Karabay, Aknur and Bolatov, Arman and Varol, Huseyin Atakan and Chan, Mei-Yen}, TITLE = {A Central Asian Food Dataset for Personalized Dietary Interventions}, JOURNAL = {Nutrients}, VOLUME = {15}, YEAR = {2023}, NUMBER = {7}, ARTICLE-NUMBER = {1728}, URL = {https://www.mdpi.com/2072-6643/15/7/1728}, ISSN = {2072-6643}, ABSTRACT = {Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on the creation of a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate the effectiveness and high accuracy of computer vision for dietary assessment.}, DOI = {10.3390/nu15071728} }

@INPROCEEDINGS{10066796, author={Kuzdeuov, Askat and Koishigarina, Darina and Varol, Huseyin Atakan}, booktitle={2023 IEEE International Conference on Big Data and Smart Computing (BigComp)}, title={AnyFace: A Data-Centric Approach For Input-Agnostic Face Detection}, year={2023}, volume={}, number={}, pages={211-218}, doi={10.1109/BigComp57234.2023.00042}}

@INPROCEEDINGS{10066768, author={Orel, Daniil and Yeshpanov, Rustem and Varol, Huseyin Atakan}, booktitle={2023 IEEE International Conference on Big Data and Smart Computing (BigComp)}, title={Speech Recognition for Turkic Languages Using Cross-Lingual Transfer Learning from Kazakh}, year={2023}, volume={}, number={}, pages={174-182}, doi={10.1109/BigComp57234.2023.00037}}

@INPROCEEDINGS{10601208, author={Akhmetov, Tolegen and Moger, Gourav and Tursynbek, Iliyas and Atakan Varol, Huseyin}, booktitle={2023 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)}, title={Thermal Perception Using Augmented Reality for Industrial Safety}, year={2023}, volume={}, number={}, pages={91-96}, keywords={Wireless communication;Industries;Visualization;Three-dimensional displays;Thermal factors;Service robots;Cameras;augmented reality;thermal camera;industrial safety;computer vision}, doi={10.1109/RAAI59955.2023.10601208}}

@INPROCEEDINGS{10601292, author={Kuzdeuov, Askat and Nurgaliyev, Shakhizat and Turmakhan, Diana and Laiyk, Nurkhan and Varol, Huseyin Atakan}, booktitle={2023 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI)}, title={Speech Command Recognition: Text-to-Speech and Speech Corpus Scraping Are All You Need}, year={2023}, volume={}, number={}, pages={286-291}, keywords={Accuracy;Speech coding;Virtual assistants;Speech recognition;Data collection;Benchmark testing;Data models;Speech commands recognition;text-to-speech;Kazakh Speech Corpus;voice commands;data-centric AI}, doi={10.1109/RAAI59955.2023.10601292}}

@Article{info14020074, AUTHOR = {Mussakhojayeva, Saida and Dauletbek, Kaisar and Yeshpanov, Rustem and Varol, Huseyin Atakan}, TITLE = {Multilingual Speech Recognition for Turkic Languages}, JOURNAL = {Information}, VOLUME = {14}, YEAR = {2023}, NUMBER = {2}, ARTICLE-NUMBER = {74}, URL = {https://www.mdpi.com/2078-2489/14/2/74}, ISSN = {2078-2489}, ABSTRACT = {The primary aim of this study was to contribute to the development of multilingual automatic speech recognition for lower-resourced Turkic languages. Ten languages—Azerbaijani, Bashkir, Chuvash, Kazakh, Kyrgyz, Sakha, Tatar, Turkish, Uyghur, and Uzbek—were considered. A total of 22 models were developed (13 monolingual and 9 multilingual). The multilingual models that were trained using joint speech data performed more robustly than the baseline monolingual models, with the best model achieving an average character and word error rate reduction of 56.7%/54.3%, respectively. The results of the experiment showed that character and word error rate reduction was more likely when multilingual models were trained with data from related Turkic languages than when they were developed using data from unrelated, non-Turkic languages, such as English and Russian. The study also presented an open-source Turkish speech corpus. The corpus contains 218.2 h of transcribed speech with 186,171 utterances and is the largest publicly available Turkish dataset of its kind. The datasets and codes used to train the models are available for download from our GitHub page.}, DOI = {10.3390/info14020074} }

@Inbook{Makhataeva2023, author="Makhataeva, Zhanat and Akhmetov, Tolegen and Varol, Huseyin Atakan", editor="Nee, Andrew Yeh Ching and Ong, Soh Khim", title="Augmented Reality for Cognitive Impairments", bookTitle="Springer Handbook of Augmented Reality", year="2023", publisher="Springer International Publishing", address="Cham", pages="765--793", abstract="Augmented reality (AR) is a rapidly developing technology that has introduced new approaches for the design of human technology interaction in various fields. However, application of AR in the area of assistive systems for cognitively impaired people is not deeply studied yet. In this chapter, we investigate the state of the art of AR in assistive systems for the cognitively impaired. Specifically, we will investigate the role of AR during the design, implementation, and performance assessment of perception and memory augmentation assistive systems. We start with a summary of various technologies utilized for the development of AR systems, including sensor and camera technology, data visualization methods, computing paradigms, and intelligent data processing algorithms. Then, we discuss the fundamental mechanisms behind human memory system and look at the examples of the first technology-based human memory and intelligence augmentation systems. We overview various methods for estimation of the human cognitive state and mental workload utilized during the evaluations of such assistive systems in human involved experimental studies. Our main objective in this work is to find out the current status, challenges, and future perspectives of AR in the research of human memory and perception augmentation systems for the people with cognitive impairments.", isbn="978-3-030-67822-7", doi="10.1007/978-3-030-67822-7_31", url="https://doi.org/10.1007/978-3-030-67822-7_31" }

@ARTICLE{10343164, author={Shakerimov, Aidar and Alizadeh, Tohid and Varol, Huseyin Atakan}, journal={IEEE Access}, title={Efficient Sim-to-Real Transfer in Reinforcement Learning Through Domain Randomization and Domain Adaptation}, year={2023}, volume={11}, number={}, pages={136809-136824}, doi={10.1109/ACCESS.2023.3339568}}

@EMBC 2023, author={Batyr Arystanbekov, Askat Kuzdeuov, Shakhizat Nurgaliyev, and Huseyin Atakan Varol}, booktitle={2023 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, title={Image Captioning for Visually Impaired and Blind: A Recipe for Lower-Resourced Languages}, year={2023}, volume={}, number={}, pages={}, doi={10.36227/techrxiv.22133894.v2}

@inproceedings {yeshpanov23_interspeech, author={Rustem Yeshpanov and Saida Mussakhojayeva and Yerbolat Khassanov}, title={{Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration}}, year=2023, booktitle={Proc. INTERSPEECH 2023}, pages={5521--5525}, doi={10.21437/Interspeech.2023-249} }

@ARTICLE{10246330, author={Makhataeva, Zhanat and Akhmetov, Tolegen and Varol, Huseyin Atakan}, journal={IEEE Transactions on Human-Machine Systems}, title={Augmented-Reality-Based Human Memory Enhancement Using Artificial Intelligence}, year={2023}, volume={}, number={}, pages={1-13}, doi={10.1109/THMS.2023.3307397}}

@INPROCEEDINGS{10312708, author={Orel, Daniil and Varol, Huseyin Atakan}, booktitle={IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society}, title={Noise-Robust Automatic Speech Recognition for Industrial and Urban Environments}, year={2023}, volume={}, number={}, pages={1-6}, doi={10.1109/IECON51785.2023.10312708}}

@INPROCEEDINGS{10311869, author={Balgabekova, Zarema and Alaran, Muslim and Varol, Huseyin Atakan}, booktitle={IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society}, title={A Data-Centric Approach for Object Recognition in Hemispherical Camera Images}, year={2023}, volume={}, number={}, pages={1-6}, doi={10.1109/IECON51785.2023.10311869}}