Multimodal AI, Sensor Fusion & Intelligent Data Systems
MultisensorProject.eu publishes practical, engineering-grade guides on multimodal AI, sensor fusion, edge AI, and the language technologies that power modern information systems. The site continues the legacy of the EU-funded MULTISENSOR research project — where techniques like multimodal content analysis, named entity recognition, and machine translation were developed for real media-monitoring use cases — and applies that perspective to today's tools and hardware.
Sensor Fusion
How intelligent systems combine data from multiple sensors into one reliable picture of the world — the core discipline behind autonomous vehicles, robotics, and drones.
- Start with the pillar: What is sensor fusion? A complete guide
- Go deeper: Sensor fusion algorithms — Kalman filters, Bayesian methods and deep learning
- Compare sensors: LiDAR vs radar
- Sensor basics: IMU sensors explained: accelerometers, gyroscopes and fusion
- Sensors: LiDAR sensors explained
- Radar: mmWave radar explained
- Depth: Time-of-flight (ToF) sensors
- Compare: Sonar vs radar
- 3D capture: Photogrammetry vs LiDAR
- Perception: LiDAR vs camera
- Algorithms: The extended Kalman filter, step by step
- Browse all sensor fusion articles
Edge AI
Running AI where the data is born: single-board computers, accelerators, and smart cameras that do inference without the cloud.
- Hardware guide: NVIDIA Jetson Orin Nano: specs, setup and first projects
- Buyer's guide: Best Raspberry Pi AI kits
- Hands-on: Raspberry Pi AI Camera setup
- Head-to-head: Raspberry Pi AI HAT+ vs NVIDIA Jetson
- Ideas: Raspberry Pi 5 projects worth building
- Concepts: Edge AI vs cloud AI
- Jetson family: Jetson Orin NX vs AGX Orin
- Accelerators: Best M.2 AI accelerators
- Status check: Google Coral TPU: is it discontinued?
- Browse all edge AI articles
Media Monitoring
Tracking what news, broadcast, and social media say — the original MULTISENSOR use case, now an industry powered by NLP at scale.
- Fundamentals: What is media monitoring? (with the AI behind it)
- Tool comparison: Best media monitoring tools
- Social focus: Best social media monitoring tools — and the free options
- Providers: Top media monitoring companies and services, types and pricing
- Concepts: Social listening vs media monitoring
- How-to: Build a media monitoring workflow
- Brand monitoring: Brand monitoring tools
- AI visibility: AI brand monitoring
- Alternatives: Brandwatch alternatives
- Alternatives: Meltwater alternatives
- Alternatives: Brand24 alternatives
- Browse all media monitoring articles
Language & Document AI
Machine translation, OCR, and document understanding — how machines read, translate, and extract meaning from text.
- Tool comparison: Best machine translation software
- Head-to-head: DeepL vs Google Translate
- Perspective: Machine translation — between dream and reality
- Evaluation: How to evaluate machine translation quality (BLEU, COMET, human review)
- OCR: Best OCR software
- How-to: OCR for PDFs: extract text from any PDF
- NLP: What is named entity recognition (NER)?
- Speech: Best AI transcription tools
- Workflow: Machine translation post-editing (MTPE)
- QA: Translation quality assurance
- Browse all language & document AI articles
Multimodal AI
Systems that combine text, images, audio, and video into one understanding — the thread that connects everything we cover.
- NLP foundations: Named entity recognition: who am I — and if so, why?
- Language understanding: Modeling human natural language understanding
- Start with the pillar: What is multimodal AI? A complete guide
- Models: Multimodal AI models explained
- Applications: Multimodal AI use cases: 12 real-world applications
- Generation: Multimodal generative AI
- Agents: Multimodal AI agents
- Browse all multimodal AI articles
Data for AI
Datasets, labeling, synthetic data, and vector databases — the raw material every AI system depends on.
- Start here: AI datasets: why they matter and where to find them
- Sources: Image datasets for machine learning
- Labeling: Best data labeling tools
- Annotation: Data annotation: process, types and tools
- Synthetic data: Synthetic data generation
- Synthetic data tools: Best synthetic data tools
- Vector search: Best vector databases
- Browse all Data for AI articles
Also on this site
Two more areas are growing: AI tools — hands-on comparisons of practical AI software — and hardware for training and running models, from GPUs to sensors — start with the best GPUs for deep learning, a GPU workstation build or the cloud GPU providers compared. New guides are published every week; see the latest articles.
From EU research project to applied AI coverage
MULTISENSOR was a European research project on multidimensional content integration: teams across Europe built systems for multimodal analysis, semantic reasoning, and multilingual media monitoring. That work lives on here in two ways. The project archive and publications preserve the original research context — deliverables, papers, and events — while our current editorial work translates the same themes into practical guidance for engineers, makers, and teams choosing AI tools today.
Start here
New to the site? These three pieces show what we do: the complete guide to sensor fusion for the fundamentals, the Jetson Orin Nano guide for hands-on edge AI, and the media monitoring tools comparison for applied NLP in the real world. Or browse everything in the latest articles.