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Project: Developing Intellectual Software-Technical Systems for Digital Signal and Medical Image Processing Using Piecewise Polynomial Basis: Theoretical and Methodological Foundations
Project Coordinators (PIs): Prof. H.N. Zaynidinov, TUIT, Uzbekistan, and Prof. Dhananjay Singh, HUFS, South Korea
Duration: 06.2021 - 05.2023
Funding Industry: Ministry of Innovative Development of the Republic of Uzbekistan
Description: The project has successfully developed intellectual software-technical systems for digital signal and medical image processing using the theoretical and methodological foundations of spline functions. Spline functions are smooth, piecewise polynomial functions that have proved highly effective for various computational tasks due to their flexibility and continuity. Throughout the project, we explored and implemented spline-based approaches in key application domains, including medical image analysis, digital signal enhancement, computer graphics, image restoration, machine vision, multimedia systems, animation, and computer game development.
Project: The Development of Theory, Methods, and Tools for the Processing of Numerical Signals and Medical Image Data Using Splines
Project Coordinators (PIs): Prof. Sh. Anarova, TUIT, Uzbekistan and Prof. Dhananjay Singh, HUFS, South Korea
Duration: 06.2022 - 05.2023
Funding Industry: Ministry of Innovative Development of the Republic of Uzbekistan
Description: This project successfully achieved its goal of developing and validating theoretical foundations, methods, and tools for the processing of numerical signals and medical image data using spline functions. Through extensive research and experimentation, we explored the mathematical properties of splines and their application in digital signal and image analysis, focusing on efficient data processing and recovery techniques.
The project outcomes include the creation of novel spline-based methods for enhancing and reconstructing signal and image data, particularly in medical imaging contexts. In addition, significant progress was made in modeling multi-core processor architectures and designing parallel algorithms optimized for spline computations. These implementations demonstrated improved computational efficiency and scalability in processing large datasets.
in addition, the research contributed to a deeper understanding of the role of splines in modern data analysis, showcasing their versatility across different domains. The developed tools and algorithmic frameworks are expected to support future advancements in medical diagnostics, signal enhancement, and high-performance computing applications. The project has laid a strong foundation for continued innovation in spline-based signal and image processing, offering both theoretical insights and practical solutions for real-world challenges.
Project: Metaverse architecture of health ailments diagnosis: To consult a doctor
Duration: 12.2020 - 12.2023
Funding Industry: NRF, Korea
Description: The goal of this project is to create a metaverse architecture that leverages gamification to remove barriers to counseling and promote the prevention and treatment of depression. This architecture allows doctors to remotely monitor patients and make more accurate diagnoses. It also enables patient counseling sessions to be recorded as video for diagnosis and treatment purposes. In this architecture, both doctors and patients can choose to represent themselves as characters, allowing for a more relaxed and interactive counseling experience. This approach can help patients feel more comfortable participating in counseling sessions and enables doctors to better assess patients' health conditions. Overall, this metaverse architecture has the potential to significantly improve the efficiency and effectiveness of counseling and treatment for depression.
Talk: Mentality Metaverse Conference 2021 - Day 3 - Dec 2 - YouTube
Project: Deep Learning Architecture for Multimodal Sentiment Analysis
Duration: 12.2020 - 12.2023
Funding Industry: NRF, Korea
Description: This project aims to address a wide range of mental health conditions that impact mood, thinking, and behavior. Depression is a particularly prevalent issue that we aim to address. We believe that artificial intelligence (AI) solutions, which involve the simulation of human intelligence processes by machines, can be particularly effective in addressing mental stress and depression. These solutions may include machine learning, natural language processing (NLP), and deep learning techniques. We believe that deep learning combined with computer vision can provide valuable insights into human emotions, body language, and behavior patterns, allowing us to better understand and address emotional stress. By using machine learning solutions to analyze behavior history, we may be able to create personalized schedules that can help alleviate mental stress and improve overall well-being.
Workshops: Mental Health Crisis and Treatment (MHCT) and AI-MHA: AI-Inspired Solutions for Mental Health Ailments: Prevention, Detection, and Treatment
Publications: Exploring Multimodal Features and Fusion for Time-Continuous Prediction of Emotional Valence and Arousal" 13th International Conference, IHCI 2020, Daegu, South Korea, December 20–22, 2021
Project: Deep Learning Architecture for Mental Health Prevention
Duration: 2.2021 - 2.2023
Funding Industry: NRF, Korea
Description: This project aims to address a wide range of mental health conditions that impact mood, thinking, and behavior, with a particular focus on depression. To this end, we are developing a deep learning platform for human emotion processes using artificial intelligence (AI) techniques such as machine learning, natural language processing (NLP), and deep learning. These AI domains have the potential to effectively combat mental stress and depression. Additionally, by leveraging deep learning combined with computer vision, we can gain a deeper understanding of human emotions, body language, and behavior patterns, allowing us to more accurately identify and address emotional stress. By using machine learning solutions to analyze behavior history, we may be able to create personalized schedules that can help alleviate mental stress and improve overall well-being. Overall, our goal is to develop an AI platform that can effectively address mental health issues and improve the well-being of individuals.
Workshops: Mental Health Crisis and Treatment (MHCT) and AI-MHA: AI-Inspired Solutions for Mental Health Ailments: Prevention, Detection, and Treatment
Project: AI-enabled Pharmaceutical Box for Mental Health Medication and Adherence
Duration: 2.2021 - 2.2023
Funding Industry: NRF, Korea
Description: This project aims to create an innovative medicine container that utilizes an application to track personal information, prescribed medications, the recommended daily dosage, and humidity and temperature conditions. The container is divided into two layers, with the second floor used for medication storage and the first floor used for dispensing the drugs in the prescribed order. The container can be controlled using Bluetooth or Wi-Fi, allowing the medications to be dispensed automatically to the patient through the use of the application. By utilizing this innovative medicine container, we hope to improve the accuracy and efficiency of medication management and ensure that patients receive the correct dosage at the appropriate times.
Workshops: Mental Health Crisis and Treatment (MHCT) and AI-MHA: AI-Inspired Solutions for Mental Health Ailments: Prevention, Detection, and Treatment
Project: GIT Diagnosis: Gastrointestinal Tract Diagnosis Device and Data Study
Duration: 03.2020 - 02.2021
Industry: COIKOSITY Private Limited, (Coikosity)
Description: This project involves the development of a low-cost, portable medical device for non-invasive diagnosis of gastrointestinal (GIT) diseases. The device is capable of monitoring Electrogastrogram (EGG) signals and can be used to diagnose stomach-related conditions. One of the key advantages of this device is that it is easy to operate and requires little to no expertise, making it suitable for use in a wide range of settings. By providing a simple and cost-effective solution for GIT diagnosis, we hope to improve access to care and facilitate earlier detection and treatment of gastrointestinal diseases.
Demo video: GIT Diagnosis on Vimeo
Korean Patent: GASTROINTESTINAL DIAGNOSTIC SYSTEM
Book Chapter: Prateeti Mukherjee and Dhananjay Singh, "The Opportunities of Blockchain in Health 4.0", Blockchain Technology for Industry 4.0 pp 149–164.
Publication:
Hakimjon Zaynidinov, Sarvar Makhmudjanov, Farkhad Rajabov, Dhananjay Singh*, "IoT-Enabled Mobile Device for Electro-gastrography Signal Processing", 12th International Conference, IHCI 2020, Daegu, South Korea, November 24–26, 2020, Part II
Project: Global Healthcare Monitoring Application: ECG, PPG, Temp., 6LoWPAN Technology
Duration: 03.2007 - 02.2010
Industry: BK21-Korea: Ubiquitous Heathcare
Description: The development of global patient health monitoring systems has been impacted by advances in wirlesess sensor networks (WSNs). IP-based wireless sensor networks (IP-WSNs) have become a popular research topic due to their ability to provide global connectivity between IP-sensor devices and IP-network services. The Internet Engineering Task Force (IETF) working group has designed a new stack, 6lowpan, which integrates IPv6 with Lowpan devices known as IP-WSN nodes. These nodes can be either reduced-function devices (RFDs) or full-function devices (FFDs). In this study, we have used RFDs as biomedical sensor (BMS) nodes and FFDs as IP-WSN nodes. We have presented the results of our novel protocols, which demonstrate improved performance of IP-WSN networks and IP-based wireless biomedical sensors (IP-WBMS) through simulation results and testing in a real-time environment using various equipment.
PhD Thesis Title: IP-Based Wireless Sensor Network for Global Healthcare Monitoring Applications
Book: Dhananjay Singh*, "Future Internet Services for e-Healthcare Monitoring Applications", of VDM Verlag Dr. Müller, publisher, Germany; ISBN:978-3-639-36608-2; July 2011, pg.172
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