TAKEDOWN (H2020 FCT-16-2015) is a European Union’s Horizon 2020 Research and Innovation Programme project under Grant Agreement No 700688. The project is concerned with the development of dynamic, multi-dimensional models for Organized Crime/Terrorism Network that reflect the complexity of individual and structural dimensions, geographical scale as well as the forms and levels of responses related to different stakeholder groups and first-line practitioners. The expected outcome of the project will be a modular TAKEDOWN Solutions Platform build as a flexible PaaS hosting digital security solutions for professionals with a centralized Security Solution Dashboard that is aggregating solution activity streams and includes digital reporting tool alert visualization’s. This project is being implemented in collaboration between the research community, public agencies, practitioners and industry (especially SMEs) providing a digital TAKEDOWN Professional Advisor to support the selection of the right solutions providers, experts, and approaches to tackle Organized Crime/Terrorism Network challenges. 2016. Araştırmacı Avrupa Konseyi
Changing Women’xxs Vulnerability tin Uganda this is an International Development Grants Programme supported Project in calibration with The John Ellerman Foundation. The project tackles systemic issues such as lack of access to education, healthcare, changing societal and women’xxs vulnerability to poverty in Uganda Uzman Diğer (Uluslararası)
South Sudan Women Concern The project aims towards capacity building of 30 groups of displaced women in Southern Sudan and provision of support to former child soldiers Yönetici Diğer (Uluslararası)
Projeler (Ulusal)
İnteraktf Dijital Solunum Oyunu ve Cihazının Geliştrilmesi ve Bronşektazili Çocuklarda Etkinliğinin İncelenmesi The project included the design and development of a spirometer for use with children with breathing difficulty. The system is attached to a computer where the spirometer readings are analyzed and recorded. The recorded readings are analyzed using Machine learning algorithms which diagnose the current breathing abilities of the patient then predict the next level required by the patient. The computer system includes a game which the patient can play; the game uses the patient breathing as an input to motivate the children to complete their breathing exercises. Araştırmacı TÜSEB
Intelligent Multi-object Detection, Identification and Tracking The aim of this work is to be able to track a target via several camera feeds from different predefined locations and sources. In this case, the idea is that a moving target will appear in a different location via visual infrastructure (Camera). Pre-trained models scan the camera feed to detect and identify targets of interest. Once a target is identified, the target information is stored in a temporary dataset containing all the information regarding the target's movement. The temporary dataset will contain data regarding actual targets and associated targets. A pre-trained model will use the dataset to continuously identify and track both the target and the associate target.
Once the targets are identified, their information is passed through to the relevant security agency. There are various methods and libraries available in Python that can assist you in accomplishing this task. One popular approach is to use image similarity algorithms.
Here's a step-by-step guide to get you started:
1. Acquire the target images: Make sure you have the all-N target images that are required for comparison. This can initially be either provided as a local file path to the images or retrieved from an online source.
2. Preprocess the images: Depending on your specific requirements, you may need to preprocess the images before performing a similarity comparison. This can include tasks such as resizing the images to a standard size, converting them to grayscale, or applying any necessary filters.
3. Choose a similarity metric: Determine the type of similarity metric you want to use to compare the images. Some common metrics include mean squared error (MSE), structural similarity index (SSIM), or feature-based methods like SIFT or SURF.
4. Calculate similarity scores: Use the chosen similarity metric to calculate the similarity scores between the target images. Various Python libraries are available for image processing and similarity calculation, such as OpenCV, sci-kit-image, or Pillow.
5. Compare and identify similar targets: Once you have the similarity scores for the target images, you can compare them and identify the most similar pair or rank them based on similarity scores. You can determine if the targets are similar or dissimilar depending on the threshold you set.
It's important to note that the effectiveness of target identification and similarity comparison depends on the specific characteristics of the targets and the chosen similarity metric. It might require experimentation and fine-tuning to achieve accurate results for your specific use case.
Proje Koordinatörü Yükseköğretim Kurumları tarafından destekli bilimsel araştırma projesi
Satellite Independent Global Positioning System “SIGPS The main purpose of the system is to complement and enhance the capabilities of conventional GPS systems. Its design focuses on ensuring uninterrupted operation, even in situations where satellite reception is unavailable or compromised. To achieve this, the system incorporates an integrated gyroscope, which enables it to function reliably in harsh conditions or when satellite connectivity is limited. By integrating a gyroscope into its architecture, the system gains the ability to maintain accurate positioning and navigation information autonomously. This self-contained feature allows it to operate independently, reducing dependence on external satellite signals for positioning data. As a result, users can rely on the system's performance even in remote areas, dense urban environments, or scenarios where direct satellite reception may be obstructed. The integrated gyroscope serves as an inertial navigation system, continuously providing updates on the device's orientation, angular velocity, and acceleration. Leveraging this data, the system can calculate its current position, velocity, and heading, regardless of satellite availability. This innovative approach ensures that users can obtain reliable and real-time location information, minimizing disruptions caused by poor satellite reception or signal blockages. The integration of the gyroscope into the system's architecture not only enhances its resilience to satellite unavailability but also offers additional benefits. It enables the system to maintain consistent and accurate positioning during temporary signal outages or when transitioning between environments with varying levels of satellite reception. Furthermore, the system's reliance on an integrated gyroscope reduces power consumption compared to conventional GPS receivers. By not constantly searching for and maintaining satellite connections, it can operate more efficiently and extend the device's battery life. This advantage is particularly significant in situations where power sources are scarce or limited, such as remote outdoor settings or prolonged missions. Araştırmacı ARAŞTIRMA PROJESİ
The project on Integrated Water Resources Management (IWRM) in the Lower Jordan Rift Valley ● Sustainable Management of Available Water Resources with Innovative Technologies. The project on Integrated Water Resources Management (IWRM) in the Lower Jordan Rift Valley is a research project sponsored by the German Federal Ministry of Education and Research (BMBF). Al Balqa University – Jordan, 2008-2010. The main idea of SMART is to include all water resources of the Lower Jordan River, namely groundwater, wastewater, saline water, and flood water into an integrated management concept. This idea is implemented with a series of test sites along both sides W and E of the Jordan Valley to consider and include different hydrogeological settings and hydrological boundary conditions. The general approach to the complex IWRM problem was tested on different problems and questions with appropriate scientific detail in specific sub-catchment areas on a local scale. Later the technologies and results are transferred and foreseen for application on a regional scale in the investigation area of Lower Jordan Valley. The selection of the local study areas was based on existing monitoring infrastructure, a set of complementary hydrologic boundary conditions and water management challenges and finally the on the suggestions and preferences of the local partners and stakeholders in the region. The SMART project has been subdivided into eleven work packages to ensure structured communication and transparent distribution of responsibilities among the partners. The executive summary lists the goals and key achievements which were obtained in each of the work packages. WP1: Coordination The project management so far has been successful in maintaining a spirit of open scientific exchange among the partner institutions and thematically associated projects in the region. A significant amount of work was required to arrive at a good contractual basis with the international partners. Project progress was monitored using the set of deliverables and milestone specified in the proposal. Danışman ARAŞTIRMA PROJESİ
Command Control Computer Communication and Intelligence (C4I) system The "Command Control Computer Communication and Intelligence (C4I) system" revolve around the development, implementation, or enhancement of a C4I system. C4I systems are designed to enable effective command and control of military or emergency response operations by integrating various technologies and communication networks. The project subjects within the broader scope of C4I systems:
1. Design and Development of a C4I System: This project could focus on designing and developing a comprehensive C4I system from scratch. It involve creating a conceptual framework, identifying the required hardware and software components, establishing communication protocols, and integrating various subsystems such as command centers, communication networks, and intelligence gathering systems.
2. Integration of Emerging Technologies in C4I Systems: This project explored the integration of emerging technologies, such as artificial intelligence (AI), machine learning, blockchain into existing C4I systems. The aim is to enhance the system's capabilities in terms of real-time data analysis, predictive modeling, secure data transmission, or improved situational awareness.
3. Cybersecurity and Resilience in C4I Systems: This project focused on addressing the cybersecurity challenges faced by C4I systems. It involved designing and implementing robust security measures, developing intrusion detection and prevention systems, conducting vulnerability assessments, and ensuring the resilience of the C4I system against cyber threats.
4. Interoperability and Standardization of C4I Systems: This project explored ways to enhance interoperability and standardization among different C4I systems used by multiple organizations or military branches.
Proje Koordinatörü ARAŞTIRMA PROJESİ
