Projects

Project Overview

The accurate detection and localization of small colorectal tumors during laparoscopic surgery presents a significant challenge for surgeons. Traditional imaging techniques often struggle to precisely identify tumor locations in real-time during minimally invasive procedures, potentially leading to incomplete resections or complications. This is particularly critical for small tumors where visual and tactile feedback is limited.

The IntelliSense project addresses this critical need by developing an optimized RFID system specifically designed for laparoscopic environments. The system combines miniaturized RFID tags implantable near tumor sites with advanced readers adapted for intra-operative use, enhanced by artificial intelligence algorithms for improved signal interpretation. Our preliminary analytical studies and laboratory tests have demonstrated the feasibility of achieving detection distances of approximately 40mm through biological tissue, while addressing challenges such as signal attenuation and electromagnetic interference in surgical environments.

This technology has the potential to significantly improve surgical outcomes by providing surgeons with precise, real-time localization of small tumors during laparoscopic colorectal surgery, ultimately enhancing patient safety and treatment effectiveness.

Project Objectives

Main Objective: To develop an optimized RFID system for laparoscopic surgery, integrate it with advanced artificial intelligence technologies, and test it on anatomical models, resulting in a functional prototype testable in advanced in-vivo scenarios on animal models.

1. Design and develop a miniaturized RFID system for laparoscopy - To create biocompatible RFID tags and optimized antenna systems capable of operating effectively in the challenging electromagnetic environment of laparoscopic surgery, addressing signal attenuation through biological tissue.
2. Develop an adapted RFID reader for intra-operative use - To design and manufacture a specialized RFID reader system optimized for surgical environments, with automatic calibration protocols and enhanced signal-to-noise ratio (SNR) optimization to ensure reliable detection in the presence of interference.
3. Implement AI software for RFID data interpretation - To develop machine learning algorithms and neural networks for advanced signal filtering, pattern detection, and real-time data analysis, significantly improving detection accuracy and reducing false positives.
4. Test the system on advanced medical specimens for clinical validation - To conduct comprehensive ex-vivo testing on animal and human tissue specimens, followed by in-vivo validation on animal models, comparing performance with conventional imaging techniques (CT, MRI, ultrasound) and preparing for TRL 4/5.

Publications & Outputs

Planned Journal Articles (Q1 Journals)

"Advanced RFID-Based Localization System for Small Tumor Detection in Laparoscopic Surgery"
Focus: Design and development of the IntelliSense RFID system for intra-operative localization of small tumors. Covers hardware design, operating room integration, and comparative precision with other methods.
Status: Planned for 2025

"Optimization of RFID Signal Processing for Biomedical Applications: A Case Study on Tumor Detection"
Focus: RFID signal processing optimization for medical applications. Covers noise filtering algorithms, signal calibration, and testing on biological phantoms.
Status: Planned for 2025

"Evaluation of RFID Sensor Performance in Biological Environments: Challenges and Solutions"
Focus: Analysis of biological environment impact on RFID system performance. Addresses signal attenuation in tissues and solutions for improving reliability.
Status: Planned for 2025

"Development of a Portable RFID-Based Device for Enhanced Laparoscopic Tumor Localization"
Focus: Creation of a portable RFID device for laparoscopic surgery. Covers modular design, medical imaging integration, and usage scenarios.
Status: Planned for 2026

"Machine Learning-Enhanced RFID Systems for Real-Time Tumor Detection: A Feasibility Study"
Focus: Using AI to improve RFID-based tumor detection. Covers classification algorithms, RFID signal pattern analysis, and experimental validation.
Status: Planned for 2026

"Comparative Study of RFID and Conventional Imaging Techniques for Small Tumor Localization"
Focus: Performance comparison of RFID with MRI, CT, and ultrasound. Analyzes sensitivity, specificity, advantages and limitations of each method.
Status: Planned for 2026

Additional Planned Publications

Additional articles are planned covering AI-driven signal enhancement using deep learning (CNN, RNN), fusion of RFID and AI for intelligent tumor localization, and comprehensive ex-vivo and in-vivo testing results on animal models.

Acknowledgment for all publications: "This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CCCDI - UEFISCDI, project number PN-IV-P7-7.1-PED-2024-0959, within PNCDI IV."

Project Overview

Cardiac rehabilitation involves delivery of structured exercise, education and risk reduction in a cost-effective manner. Robust evidence demonstrates it reduces mortality up to 25%, improves functional capacity, and decreases re-hospitalization. However, following cardiac surgery, patients often experience difficulties with the rehabilitation process, finding it challenging and lacking motivation for rehabilitation activities.

With the global population aged 65 and over projected to rise by 207 percent by 2050, the need for cardiac rehabilitation will significantly increase. Traditional cardiac rehabilitation programs are grossly under-used worldwide, with only 38.8% of countries globally having CR programs available. To address these challenges, the CardioVR-ReTone project aims to provide motor recovery for patients following cardiac surgery or a major cardiac event, enabling them to live normal, active, and independent lives through an innovative robotic solution enhanced with virtual reality technology.

Project Objectives

1. Design and manufacture the prototype of robotic exoskeleton system - To create the CardioVR-ReTone exoskeleton with 12 degrees of freedom (6 DoFs on each arm) featuring a symmetric structure for upper limbs, enabling natural shoulder and elbow movements essential for cardiac rehabilitation exercises.
2. Design and development of VR module - To develop non-immersive virtual reality exergame functionalities that interface with user requests and actions triggered from the robotic exoskeleton, providing engaging and motivating rehabilitation experiences.
3. Integrate all components of CardioVR-ReTone system - To successfully integrate the Upper Body Robotic Exoskeleton with the Non-Immersive Virtual Reality Exergame system, creating a cohesive rehabilitation platform.
4. Demonstration of concept validity and testing - To validate the CardioVR-ReTone prototype functionalities in research laboratories and clinical environments, achieving Technology Readiness Level 4 (TRL4) through comprehensive testing with cardiac surgery patients.
5. Dissemination of results - To publish findings in high-impact journals, present at international conferences, and share results with the broader scientific community to advance the field of robotic-assisted cardiac rehabilitation.

Publications & Outputs

Journal Articles

Mocan, B.; Schonstein, C.; Neamtu, C.; Murar, M.; Fulea, M.; Comes, R.; Mocan, M. (2022). CardioVR-ReTone—Robotic Exoskeleton for Upper Limb Rehabilitation following Open Heart Surgery: Design, Modelling, and Control. Symmetry 14(1), 81. https://doi.org/10.3390/sym14010081

Mocan, B.; Fulea, M.; Murar, M.; Brad, S. (2021). Cardiac Rehabilitation Early after Sternotomy Using New Assistive VR-Enhanced Robotic Exoskeleton—Study Protocol for a Randomised Controlled Trial. International Journal of Environmental Research and Public Health 18(22), 11922. https://doi.org/10.3390/ijerph182211922

Conference Presentations

Project results were presented at multiple national and international conferences including seminars, workshops focused on robotics, rehabilitation technology, and cardiovascular medicine. The research team actively participated in scientific network meetings to disseminate findings to the broader research community.

Technical Outputs

The project produced comprehensive technical documentation including kinematic and dynamic models, control system specifications, VR exergame software, clinical trial protocols, and complete electromechanical design specifications for the 12-DoF exoskeleton system. Patent filing and licensing opportunities were explored for key technological innovations.

Project Overview

The urgent need for sustainable lifestyles has emerged as one of the most critical challenges facing humanity today. As global environmental crises intensify, the role of higher education in fostering sustainable mindsets and preparing students to address these challenges becomes increasingly vital. However, many educational institutions struggle to effectively integrate sustainability principles into their curricula and inspire genuine behavioral change among students and faculty.

The SMART-HEAD project addresses this gap through an innovative digital approach that has the potential to transform how students perceive and apply sustainability principles in their daily lives. By leveraging artificial intelligence and modern digital technologies, the project creates engaging, accessible educational resources that resonate with today's digitally-native student population. The initiative recognizes that sustainable development requires not just knowledge transfer, but fundamental shifts in values, attitudes, and behaviors.

Through collaboration among leading technical universities across Europe, SMART-HEAD develops a comprehensive digital ecosystem specifically designed for sustainability education. The project emphasizes multicultural and multilingual exchange, ensuring that sustainability education transcends borders and cultural contexts. Most significantly, SMART-HEAD aims to catalyze a profound change in the value systems of students and academics, fostering intrinsic motivation toward sustainable practices that will extend far beyond their academic careers into their personal and professional lives.

Project Objectives

Main Objective: To transform how students and academics perceive and apply sustainability principles through an innovative AI-supported digital ecosystem, fostering lasting change in value systems and preparing participants for active engagement in solving global environmental challenges.

Expected Outcomes

1. Digital ecosystem for sustainable development education - To create a comprehensive, AI-enhanced digital platform that provides accessible, engaging educational resources on sustainability topics, integrating modern pedagogical approaches with cutting-edge technology to deliver personalized learning experiences tailored to individual student needs and learning styles.
2. Methodology for teaching through digital resources - To develop and validate innovative teaching methodologies specifically designed for digital sustainability education, incorporating best practices in online pedagogy, active learning strategies, and assessment techniques that effectively measure both knowledge acquisition and behavioral change toward sustainable practices.
3. Improved multicultural and multilingual exchange in sustainability - To facilitate cross-cultural dialogue and knowledge sharing among students and academics from diverse European backgrounds, creating a vibrant international community of practice where sustainability concepts are explored through multiple cultural lenses and language barriers are overcome through technology-enabled communication.
4. Enhanced awareness and engagement toward sustainable lifestyles - To significantly increase students' understanding of sustainability challenges and their personal connection to these issues, translating awareness into concrete actions and sustainable behaviors that persist beyond formal education into personal and professional life.
5. Impact on sustainable development and digital transformation - To demonstrate measurable positive impact on both institutional digital capabilities and broader sustainable development goals, establishing a replicable model for integrating AI-supported education with sustainability objectives across European higher education institutions.

Deliverables & Dissemination

Planned Project Outputs

• Digital Learning Ecosystem: Comprehensive AI-supported platform with personalized learning pathways, adaptive assessments, and interactive sustainability modules accessible to students across partner institutions and beyond
• Teaching Methodology Guidelines: Detailed documentation of innovative pedagogical approaches for digital sustainability education, including implementation guides, assessment frameworks, and quality assurance protocols
• Open Educational Resources: Extensive library of multilingual sustainability learning materials, case studies, and multimedia content available as open access resources for the broader educational community
• Impact Assessment Reports: Comprehensive evaluation studies measuring changes in student attitudes, behaviors, and value systems, along with analysis of institutional capacity development
• Policy Recommendations: Guidelines for integrating AI-supported sustainability education into higher education institutions across Europe, addressing technical, pedagogical, and organizational considerations

Dissemination Strategy

Project results will be shared through multiple channels including academic conferences, peer-reviewed journal publications, webinars and online workshops for educators, presentations at sustainability and educational technology events, social media campaigns targeting student audiences, and the Erasmus+ Project Results Platform. All deliverables will be made freely available to maximize impact and support widespread adoption of project innovations.

Publications & Updates

As the project progresses through 2025-2028, this section will be updated with:

• Academic publications resulting from project research and evaluation
• Conference presentations and workshop materials
• Technical documentation and implementation guides
• Case studies and success stories from partner institutions
• Links to the digital ecosystem and open educational resources

Check back regularly for updates as the project develops and produces tangible results.

Project Overview

Efficient palletization represents a critical challenge in modern manufacturing and logistics operations. The optimal arrangement of boxes on pallets directly impacts storage space utilization, transportation costs, handling efficiency, and product safety during shipping. However, finding optimal packing solutions is computationally complex (an NP-complete problem), and traditional approaches often rely on manual planning or rigid pre-defined patterns that cannot adapt to varying product dimensions and operational requirements.

The Expert System for Smart Robots project began in 2013 as a research collaboration between our research group and CSi Industries B.V. Netherlands. Initially conceived to develop an algorithm for optimal box arrangement on pallets, the project scope expanded significantly through 2016, evolving into a comprehensive software solution that became the "companion" system for CSi's robotic palletizing cells. The system addresses the needs of two distinct user groups: sales representatives requiring technical configuration capabilities, and cell operators needing straightforward operational control with minimal technical knowledge.

The resulting application provides intelligent pattern generation using proprietary heuristic algorithms, real-time 3D visualization of pallets and robotic cells, intuitive touch-optimized interfaces for tablet operation, automatic robot program code generation, and comprehensive hardware configuration management. This dynamically reconfigurable solution delivers the exact optimal palletization pattern precisely when needed, transforming how CSi Industries configures, sells, and operates their robotic palletizing systems.

Project Objectives

Main Objective: To design and develop an advanced software application for managing palletization patterns and hardware configurations in flexible robotic palletizing cells, providing both optimal packing solutions and comprehensive system configuration capabilities.

1. Develop intelligent pattern generation algorithms - To create proprietary heuristic-based algorithms that generate near-optimal box arrangements on pallets, addressing the NP-complete nature of the packing problem while providing practical solutions that maximize pallet surface utilization, minimize robot movements, and respect operational constraints.
2. Create intuitive user interfaces for diverse users - To design and implement tablet-optimized, touch-friendly interfaces serving both technically proficient sales representatives configuring custom palletizing solutions and operators with minimal technical knowledge running day-to-day cell operations.
3. Implement comprehensive 3D visualization - To provide real-time 3D visualization of palletization patterns, loaded pallets, and complete robotic cell configurations with interactive controls for rotation, zooming, and detailed inspection to support both sales presentations and operational validation.
4. Enable automatic robot program generation - To develop seamless integration with robot control systems through automatic generation of robot movement programs based on palletization patterns, including coordinate transformation and interfacing with PLC controllers.
5. Provide flexible hardware configuration management - To create a system for managing modular robotic cell configurations including robot selection, end-effector types, conveyors, safety systems, and other components with automatic compatibility verification and performance calculation.

Results & Key Features

Core System Capabilities

• Project-based organization: Complete project management system where each project represents a palletization pattern for a product batch, with support for unlimited projects and single customizable hardware configuration
• Comprehensive input management: Detailed specification of product name, pattern number, box dimensions and weight, pallet dimensions, maximum stack height, inter-box spacing (with 3D visualization), overhang/underhang options, packaging structure selection (one-block, two-block, three-block), multi-grabbing settings (up to 20 boxes), and feeding direction
• Intelligent pattern generation: Proprietary heuristic-based algorithm generating numerous patterns by dividing pallets into blocks and sub-blocks, then filling with similarly-oriented boxes, exploring all reasonable combinations up to 5-block complexity, with automatic calculation of surface utilization percentage
• Pattern customization: Manual pattern creation, fine-tuning of generated patterns including box coordinate adjustment with automatic collision detection, 180-degree rotation for label positioning, drag-and-drop box manipulation, and group movement options
• Layer-based management: Patterns managed at layer level with flexible layer stacking to build complete pallets, cardboard sheet insertion between layers, and maximum height enforcement

Advanced Features

• 3D visualization: Real-time 3D rendering of pallets and patterns with interactive rotation, zoom, top/bottom/side views, and pick-point visualization for validation
• Robot palletizing order: Automatic calculation of collision-free palletizing sequences with visualization and manual override capability
• Multi-language menu system: Internationalized interface supporting multiple languages for global deployment
• Customizable visualization: Color selection for pattern display and configurable visual preferences
• Data export: Pattern structure export as data files for PLC interface and robot program generation
• Performance metrics: Automatic calculation of total packages per pallet, number of blocks per layer, block dimensions, cycles per layer, volume utilization, area utilization, optimum pattern recommendations, palletizing time, throughput (boxes/hour, pallets/shift), and productivity indicators

Hardware Configuration Management

The system includes comprehensive hardware configuration management with modular standard assembly lists, 3D cell layout visualization with module highlighting, automatic compatibility checking between components (robots, end-effectors, bases, conveyors, safety systems), and management of future options and incompatibilities as user-defined information.

Impact & Industrial Application

The Expert System for Smart Robots transformed CSi Industries' approach to robotic palletizing systems, providing significant competitive advantages in both sales and operations. The system became an integral tool for sales representatives, enabling them to rapidly configure custom palletizing solutions during customer meetings, visualize complete cell layouts in 3D, and calculate precise performance metrics including throughput, cycle times, and productivity measures. This capability dramatically shortened the sales cycle and improved proposal quality.

For end users operating CSi palletizing cells, the application provided unprecedented flexibility through dynamically reconfigurable palletization patterns. Operators could easily adapt to new products or box dimensions without requiring external engineering support or lengthy reconfiguration processes. The intuitive tablet-based interface made the system accessible to operators with minimal technical training, reducing operational complexity and improving efficiency.

The automatic robot program generation capability eliminated manual programming efforts, significantly reduced setup times for new products, and virtually eliminated human errors in coordinate specification. The seamless integration between pattern design and robot execution created a truly plug-and-play experience for changing palletization configurations.

From a technical perspective, the project successfully addressed an NP-complete computational problem with practical heuristic solutions that consistently delivered optimal or near-optimal results. The pattern generation algorithm's ability to explore complex multi-block arrangements while maintaining computational efficiency represented a significant achievement. Testing validated that the system identified maximum-density packing solutions across diverse box and pallet dimensions.

The project exemplifies successful university-industry collaboration, demonstrating how academic research in algorithms, computer graphics, and human-computer interaction can be translated into commercially valuable industrial software. The three-year project duration reflected both the initial technical challenges and the recognition of expanding opportunities as the system's capabilities grew, ultimately delivering a comprehensive solution far exceeding the original scope of simple pattern optimization.

Project Overview

Leading businesses towards sustainable development represents the highest challenge for SMEs in today's dynamic and unpredictable market environment. Small and medium enterprises operate in contexts characterized by high-frequency crisis points, high-intensity conflicts, complex problems, and severe resource constraints. To survive and grow, SMEs must continuously innovate across all key segments of their business systems.

However, most SMEs lack formalized standards and structured approaches for managing innovation systematically. Innovation often remains the domain of specialists rather than becoming an accessible, everyday practice for all employees. The innDrive project addressed this critical gap by developing a comprehensive information system that brings together all perspectives and areas describing innovation under a single integrated platform.

The system provides SMEs with consistent models, procedures, specialized tools for innovation, roadmaps, expert modules for creative problem-solving, and knowledge management capabilities. Through ontology-based search engines operating across structured and unstructured databases, the platform guides companies toward mature solutions for various crisis points across all business processes, fostering a culture of continuous innovation and increasing organizational agility.

Project Objectives

Main Objective: To develop a novel information system based on a multi-layer innovation model for SMEs, providing comprehensive support for implementing multi-dimensional innovation management systems across all key business processes.

1. Develop an innovation management standard and integrated software system (innExpert) - Originally planned to create a standard for innovation management suited to Romanian economic needs. Following the emergence of CEN/TS 16555 standard in 2015, the objective was reconsidered to develop a unique dual-perspective evaluation approach: combining top-down (managerial) and project-based (bottom-up from employees directly involved in innovation) assessments of innovation management performance.
2. Create an ontology-based knowledge center (innKnow) - To provide a comprehensive guidance package of methods, methodologies, models, procedures, case studies and rules in a software-based knowledge center, managing all platform information through ontology-driven architecture and providing context-sensitive help for evaluation questionnaires.
3. Develop an extensive software toolbox for creative problem-solving (innPath) - To create and integrate a consistent set of innovation tools including requirements management, performance characteristics management, functionality management, modular structure management, AHP prioritization, QFD quality planning, TRIZ contradiction matrix, and cause-effect diagrams.

Publications & Outputs

Technical Documentation

The project produced extensive technical documentation including detailed specifications for the innDrive eXplorer tool (for EASME utilization in Enterprise Europe Network for Key Account Management in H2020 SME Instrument), comprehensive implementation reports for the platform, ontology, and innovation tools, evaluation questionnaires for assessing innovation capacity, correlation matrices linking questionnaire responses to evaluation model directions, and paradigm shift justification documentation.

Software Deliverables

• innExpert Platform: Complete web-based evaluation and reporting system with user management, project workflow, and automated scoring
• innKnow Knowledge Center: Ontology-based knowledge management system with contextual help and searchable innovation resources
• innPath Toolbox: Integrated suite of eight innovation and problem-solving tools including requirements management, AHP, QFD, TRIZ, and cause-effect analysis
• innDrive eXplorer: Standalone tool for use by EASME in European Enterprise Network

Key Features and Capabilities

The system supports three types of evaluation projects (top-down, bottom-up, and innovation tools), multiple user roles with granular permissions, comprehensive project lifecycle management from initiation to final reporting, automated generation of detailed PDF reports with graphical analysis, action planning with task assignment and tracking, benchmarking capabilities for comparing multiple projects, complete audit logging of all activities, and integration of European Commission models (CoachCOM2020) and standards (CEN/TS 16555).