Research Projects

The MareNostrum Experimental Exascale Platform (MEEP) is a flexible FPGA-based emulation platform that will explore hardware/software co-design for Exascale Supercomputers and other hardware targets, based on European-developed IP. MEEP provides two very important functions: 1) An evaluation platform of pre-silicon IP and ideas, at speed and scale, and 2) A software development and experimentation platform to enable software readiness for new hardware. MEEP enables software development, accelerating software maturity, compared to the limitations of software simulation. IP can be tested and validated before moving to silicon, saving time and money.

The HumanE AI Network will leverage the synergies between the involved centers of excellence to develop the scientific foundations and technological breakthroughs needed to shape the AI revolution in a direction that is beneficial to humans both individually and societally, and that adheres to European ethical values and social, cultural, legal, and political norms. The core challenge is the development of robust, trustworthy AI systems capable of what could be described as “understanding” humans, adapting to complex real-world environments, and appropriately interacting in complex social settings. The aim is to facilitate AI systems that enhance human capabilities and empower individuals and society as a whole while respecting human autonomy and self-determination. The HumanE AI Net project will engender the mobilization of a research landscape far beyond direct project funding, involve and engage the European industry, reach out to relevant social stakeholders, and create a unique innovation ecosystem that provides a manyfold return on investment for the European economy and society. 

The WAKeMeUP project objective is to set up a pilot line for advanced microcontrollers with embedded non-volatile memory, design, and manufacturing for the prototyping of innovative applications for the smart mobility and smart society domains.

The objective of LIGHTest is to create a global cross-domain trust infrastructure that renders it transparent and easy for verifiers to evaluate electronic transactions. By querying different trust authorities world-wide and combining trust aspects related to identity, business, reputation, etc., it will become possible to conduct domain-specific trust decisions.

This is achieved by reusing existing governance, organization, infrastructure, standards, software, community, and know-how of the existing Domain Name System, combined with new innovative building blocks. This approach allows an efficient global rollout of a solution that assists decision-makers in their trust decisions. By integrating mobile identities into the scheme, LIGHTest also enables domain-specific assessments on Levels of Assurance for these identities.

Against the background of the regulation 2014/910/EU on electronic identification (eID) and trusted services for electronic transactions in the internal market (eIDAS), the FutureTrust project aims to support the practical implementation of the regulation in Europe and beyond. For this purpose, the FutureTrust project addresses the need for globally interoperable solutions through: 1) basic research with respect to the foundations of trust and trustworthiness, with the aim of developing new, widely compatible trust models or improving existing models, 2) actively driving the standardisation process, and 3) providing Open Source software components and trustworthy services as a functional base for fast adoption of standards and solutions. 

In this pioneering NATO project, we have established a comprehensive Smart Grid Test Bed Laboratory dedicated to Critical infrastructure. This lab serves as a dynamic environment designed to simulate, evaluate, and strengthen the resilience of Smart grids, the future of energy distribution.

The primary objective of this project was to meticulously identify potential vulnerabilities within smart grid infrastructures and exploit them with the same techniques a potential adversary might use. The unique combination of red-teaming and black-box testing methods was instrumental to this endeavor. In a Red Team exercise, our specialized team adopts the mindset of an adversary, utilizing all accessible resources and tactics to penetrate the system. Complementing this, our black-box testing approach added an additional layer of scrutiny where the internal structure/design/implementation of the item being tested is not known to the tester.

By focusing on these vulnerabilities from a dual perspective, we effectively mimic the strategies and tactics of potential hostile actors. This multi-pronged approach enables us to anticipate and counter the wide variety of threats that Smart Grids might face, enhancing the grid's overall security posture.

The project has not only allowed us to identify and rectify potential vulnerabilities but has also provided invaluable insight into the operational efficiency and reliability of smart grids. This paves the way for improved security protocols (especially IEC61850) and the development of robust preventative measures. It further helps in the design of swift, effective response strategies to any potential infrastructural threats.

The outputs of this project are of considerable value to NATO member states, energy corporations, and other stakeholders in the smart grid ecosystem. Ultimately, the lessons learned from this Smart Grid Test Bed Laboratory project contributed significantly to the assurance of safe, secure, and reliable energy infrastructures across the globe.

NYIT Research Assistant Project: 

Introduction:

This research project aims to investigate the behavior of smartphone batteries, specifically Android OS-based smartphones when subjected to malicious attacks. By observing the signals and attitudes exhibited by the battery during such attacks, we aim to gain insights into the potential vulnerabilities and impacts on the device's power consumption and overall performance. This project encompasses two main steps: evaluating the accuracy of online power estimation and automatic battery behavior-based power model generation, followed by the analysis of battery signals and Linux kernel responses.

Objective:

The primary objective of this project is to understand the implications of malicious attacks on the battery behavior of Android OS-based smartphones. By analyzing the battery signals and the responses of the Linux kernel, we aim to identify potential changes in power consumption patterns and detect any anomalous behaviors resulting from these attacks. This research contributes to enhancing our understanding of the vulnerabilities of smartphone batteries and developing effective defense mechanisms against such attacks.

Methodology:

Accuracy Evaluation of Online Power Estimation: In this step, we analyzed existing methods for estimating power consumption in real-time on Android OS-based smartphones. We evaluated the accuracy of these techniques by comparing the estimated power consumption values with actual power measurements. This evaluation provided insights into the reliability and effectiveness of online power estimation approaches.

Automatic Battery Behavior-Based Power Model Generation: To develop a comprehensive understanding of battery behavior, we explored the generation of power models based on battery behavior patterns. By analyzing various factors such as battery voltage, current, temperature, and other relevant signals, we constructed a model that accurately represents the power consumption dynamics of the smartphone under normal conditions.

Investigation of Battery Signals and Linux Kernel Responses: Once we establish a baseline power model, we subject the Android OS-based smartphone to controlled malicious attacks. We monitor and record the battery signals, including voltage fluctuations, current variations, and temperature changes. Additionally, we analyze the responses of the Linux kernel during these attacks. By comparing the observed behaviors with the established power model, we can identify any deviations caused by the malicious attacks.

Expected Outcomes and Impact:

The research outcomes of this project provided valuable insights into the behavior of smartphone batteries under malicious attacks. By understanding the signals and attitudes exhibited by the battery, we can enhance the development of robust defense mechanisms against such attacks. The findings contributed to the field of smartphone security, enabling the design of more secure Android OS-based smartphones and assisting in the development of effective countermeasures to protect users' devices and data.

Conclusion:

This research project aims to investigate the impact of malicious attacks on the battery behavior of Android OS-based smartphones. By analyzing battery signals, Linux kernel responses, and evaluating power estimation techniques, we seek to enhance our understanding of smartphone vulnerabilities and develop strategies for improving device security. The outcomes of this project have already contributed to advancements in smartphone security and assisted in safeguarding users' privacy and data integrity.

Graduate Project:

The contemporary technological landscape is punctuated by increasing demand for effective vending machine systems capable of dispensing a diverse array of small products contingent upon coin insertions. Traditionally, these machines have been implemented via microcontrollers and FPGA (Field-Programmable Gate Array) boards. This project advocates for an innovative implementation method that leverages the efficiency and power-saving attributes of FPGA-based systems over traditional microcontroller-based vending machines.

Our research introduces an advanced and efficient algorithm specifically engineered for FPGA-based vending machine systems. This novel vending machine model, underscored by FPGA technology, offers prompt responses and operates at a significantly lower power consumption level compared to its microcontroller-based counterparts.

The FPGA-based vending machine algorithm supports the dispensation of four different product types and accepts three distinct coin denominations. It exhibits intelligent coin sequencing, facilitating the acceptance of input coins in any given sequence. In addition, it is designed to deliver the chosen product when the required amount is deposited, as well as to return any excess amount as change, if the input amount surpasses the product's price. The system also comes with an advanced cancellation feature which allows a user to withdraw their request at any given moment, with the assurance of a full refund in the absence of a product.

This algorithm was constructed using Verilog HDL (Hardware Description Language), and its functionality was verified through rigorous simulation using the Xilinx ISE (Integrated Software Environment) simulator tool. Future research directions include refining the algorithm to accommodate an expanded product catalog and a wider range of coin denominations, thus enhancing the adaptability and commercial viability of the system.

Undergraduate Project: 

The project at hand entails the design and implementation of an autonomous, line-tracing robot capable of accurately identifying and tracking a distinct linear path in a contrasting background. The aim is to create a robot that can proficiently navigate along a black line present in a predominantly white environment, though the principle is equally applicable to inverse scenarios.

This autonomous robot relies on robust and precise line detection algorithms to effectively trace its designated path. It is meticulously designed to demonstrate an adaptive navigation capacity, thus ensuring reliable functioning in diverse environments.

In scenarios involving intersection points or 'cross-overs', where multiple onward paths emerge, the robot adheres to a predetermined trajectory. Predominantly, it is programmed to make a rightward maneuver whenever it encounters a cross-over or a Y-shaped turn, thereby exhibiting path optimization.

While the current configuration emphasizes following a black line on a white background and adopting right-turns at junctions, the flexibility and adaptability of the robotic system allow for considerable alterations. Users can introduce suitable modifications to the system to accommodate alternative navigation requirements and environmental conditions. This scalability feature enhances the practical utility of the robot in a wide range of real-world applications.

In summary, this project underscores the development of a sophisticated line-tracing robotic system that amalgamates precision, adaptability, and path optimization, offering versatile applications in automated navigation and beyond.