Making Sense of Sensors 1st Edition by Omesh Tickoo, Ravi Iyer ISBN 1430265930 9781430265931

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ISBN 10:  1430265930
ISBN 13: 978-1430265931
Author:      Omesh Tickoo, Ravi Iyer

Making Sense of Sensors 1s Table of contents:

Chapter 1: Introducing the Pipeline

• Motivation: Introduction to the need for efficient data processing pipelines in recognition tasks.
• Next Level of Data Abstractions: Discussing advanced data models and abstractions for recognition systems.
• Operations: Key operations used in the recognition pipeline.
• Constraints and Parameters: Overview of factors like resource limitations and system constraints.
• Physical Platforms: The role of physical platforms (hardware) in data processing and recognition.
• Summary: Key points and takeaways.
• References: Relevant citations and sources.

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Chapter 2: From Data to Recognition

• Sensor Types and Levels of Recognition: Different sensors (inertial, audio, visual) and how they contribute to recognition.
  • Inertial Measurement Unit (IMU): Details about accelerometers, gyroscopes, and their role in gesture recognition.
  • Audio Sensors: Covers audio classification, voice activity detection, and speech recognition (ASR, NLP).
  • Visual Sensors: Object and gesture recognition, video summarization.
  • Other Sensors: Overview of additional sensors like proximity, location, and chemical sensors.
• Summary: Recap of the key technologies in data collection for recognition.
• References: Relevant sources and citations.

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Chapter 3: Multimodal Recognition

• Why Multi-modality: The benefits of combining multiple sensor types (audio, visual, etc.) for improved recognition.
• Multimodality Flavors: Various models for combining sensor data.
  • Coupling-based Classification: Combining data streams for classification.
  • Dasarathy Model: A specific framework for multimodal recognition.
  • Sensor Configuration Model: Different ways to configure and integrate sensors.
• Example Implementations: Real-world applications and case studies.
  • Semantic Fusion, Tight Fusion, Restricted Recognition: Methods for fusing multimodal data.
• Mathematical Approaches for Sensor Fusion: Different approaches for sensor fusion, including estimation and inferencing.
• Summary: Key points about the importance and methods of multimodal recognition.
• References: Citations for further reading.

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Chapter 4: Contextual Recognition

• Relationship between Context and Recognition: How context enhances recognition accuracy.
  • Rule-based Systems, Knowledge-based Systems: Approaches for incorporating context into machine learning systems.
• Understanding Context: Roles that context plays in improving recognition.
• Incorporating Context in Recognition: Methods for embedding context in recognition systems.
• Motivation from Human Recognition: Drawing parallels from human perception to improve machine recognition.
  • Image-based and Non-image-based Contextual Recognition: Different ways context can be utilized in recognition tasks.
• Representing Context: Strategies for representing contextual information computationally.
• Summary: Overview of how context improves recognition systems.
• References: Further reading on contextual recognition.

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Chapter 5: Extracting and Representing Relationships

• High-level View of Relationship Extraction: How relationships are identified and represented from data, particularly text.
• Relationship Extraction Methods:
  • Knowledge-based Methods: Domain-dependent and independent approaches to relationship extraction.
  • Supervised Methods: Feature-based and kernel-based techniques for extracting relationships.
  • Semi-supervised Methods: Techniques like DIPRE, Snowball, and KnowItAll for extracting relationships with limited labeled data.
• NEIL (Never Ending Image Learning): A method for continuously learning relationships from images.
• Summary: Key methods and challenges in relationship extraction.
• References: Relevant resources.

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Chapter 6: Knowledge and Ontologies

• Relationship Representation using RDF: How relationships can be represented using Resource Description Framework.
• Databases and Knowledge Graphs: Overview of databases that store relationships (e.g., Freebase, ConceptNet, Google’s Knowledge Graph).
• Semantic Web and Ontologies: Importance of ontologies in representing structured knowledge.
  • Ontology Components and Languages: Details of the structure and language used for defining ontologies.
• Summary: Recap of the role of ontologies and knowledge bases in improving recognition systems.
• References: Key references for further reading.

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Chapter 7: End-to-End System Architecture Implications

• Platform Data Processing Considerations: Factors such as compute power, battery life, memory, and interactivity that influence platform design.
• End-to-End System Partitioning and Architecture: Overview of how data processing tasks are distributed across various platforms (sensor nodes, wearable, phone, gateway, cloud).
• End-to-End Processing & Mapping Examples: Case studies demonstrating the mapping of different types of data processing (speech, visual, etc.).
• Programmability Considerations for End-to-End Partitioning: Programming challenges and solutions for distributed systems.
• Summary and Future Opportunities: Overview of challenges and potential future developments.
• Conclusion: Closing thoughts on the evolving field of recognition systems.
• References: Further citations for deeper exploration.

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