NEC Laboratories America, Inc.

Systems and methods that utilize optical fibers as sensing elements or for transmitting sensing signals, often for distributed monitoring of environmental conditions, phase changes, or integrating sensing with communication.

Methods and systems that identify unusual or suspicious patterns in data streams, often leveraging machine learning models trained on normal behavior, to detect threats, faults, or significant events as they occur.

Methods and systems for improving the quality of video streams, generating intermediate frames, or continuously locating and following objects within a sequence of images, even under occlusion.

Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.

Systems and methods utilizing artificial intelligence, particularly large language models and neural networks, to extract, summarize, generate, or categorize information from unstructured or semi-structured data sources.

Utilizing machine learning, particularly deep learning, to analyze medical data such as images, sensor readings, or physiological signals for disease prediction, diagnosis, or treatment assessment.

Application of machine learning models to process complex data and generate actionable insights, predictions, or classifications that inform or automate decision-making processes in various domains like healthcare, business, or industrial control.

Techniques utilizing deep learning models like Generative Adversarial Networks (GANs) or diffusion models to create new images, modify existing ones, or generate synthetic data based on various inputs or conditions.

Algorithms and hardware optimizations for rapidly identifying and characterizing relevant visual features (e.g., objects, motion, gradients) from images or video streams, often integrating machine learning for feature representation and recognition, with a focus on real-time performance and reduced computational cost.

Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.

Techniques for monitoring system components and behaviors to anticipate failures, performance degradation, or anomalies, often leveraging machine learning for pattern recognition and forecasting.

Methods and apparatus for detecting objects and determining their three-dimensional position and orientation (pose) using imagery or point cloud data, often for navigation, surveying, or environmental understanding.

Techniques for combining data from disparate sensor types (e.g., cameras, radar, mobile device signals) to achieve a more robust and comprehensive understanding of an environment or subject, often leveraging machine learning for interpretation and correlation.

Integration of optical sensors, particularly for biometrics or other surface interactions, beneath a display or protective cover, requiring specialized optical paths, illumination, and packaging.

Development and optimization of novel neural network layers or architectures specifically designed to improve performance or efficiency for computer vision tasks.

Algorithms and systems for generating, optimizing, and executing trajectories for autonomous vehicles or robots to move through an environment, often involving obstacle avoidance, route validation, and goal reaching.

Applying computational methods, often involving machine learning and multiomics data, to design, analyze, and understand biomolecules, genetic sequences, or complex biological systems.

Computational methods and systems for analyzing biological data (e.g., genomic, proteomic, clinical) to diagnose diseases, predict patient prognosis, assess treatment response, or stratify patients for therapy.

Digital platforms and systems that deliver tailored therapeutic interventions, guidance, or recommendations to patients based on their individual health data, biometric feedback, and computational models (e.g., AI/ML, physiological simulations).

Systems that combine data from multiple camera sensors or capture multiple images from different perspectives or qualities, often involving image processing techniques like synthesis to create enhanced or comprehensive views.

Techniques and hardware for autonomous systems to gather and interpret data about their surroundings, including obstacle detection, object recognition, and depth estimation, to inform control decisions.

Systems and methods for automating the lifecycle of machine learning models, including pipeline deployment, model management, versioning, and configuring for different inference environments.

Methods and systems for identifying, extracting, and structuring specific entities, relationships, or insights from text-based documents, often involving techniques like named entity recognition, relation extraction, or summarization.

Algorithms and systems for planning and executing complex vehicle maneuvers, often involving cooperation with other vehicles or infrastructure, to optimize traffic flow, avoid collisions, or navigate challenging scenarios. This includes lane changes, cut-ins, and traffic congestion.

Automated systems using image processing and artificial intelligence to identify, classify, and assess the extent of damage to structures or objects, supporting maintenance or insurance claims.

Methods for training machine learning models across multiple decentralized devices or servers while keeping data localized, often involving aggregation of model parameters and secure communication.

Systems and methods for enhancing the safety of vulnerable road users (pedestrians, cyclists) by improving their detection, prediction, and precise localization relative to the vehicle, often leveraging communication technologies and specialized markers.

AI systems designed to engage in natural language dialogue, maintain conversation state, understand user intent, and generate relevant responses, often across multiple communication channels or modalities.

Involves systems designed to automatically detect errors or failures and initiate predefined or intelligent corrective actions, recovery procedures, or notifications to minimize downtime and manual intervention.

Techniques and hardware architectures designed to efficiently generate and display complex 3D graphics, particularly for interactive applications like virtual reality, focusing on speed and visual quality.

Advanced sensing techniques leveraging quantum phenomena or highly sensitive optical methods, often involving interferometry, squeezed states, or exceptional points, to achieve enhanced measurement sensitivity for physical parameters.

Design and implementation of capacitive sensors, including methods for improving accuracy, reducing power consumption, compensating for environmental variations (like temperature), and analyzing complex displacement interactions.

Systems that process data to provide personalized recommendations, predict events, or automate decision-making processes based on learned patterns, user behavior, or environmental factors.

Systems and methods for non-invasive or minimally invasive collection and analysis of physiological data (e.g., blood pressure, electrolytes, genetic markers, B cell repertoire) to assess patient health status, screen for conditions, or aid in diagnosis.