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Physical Sciences · Computer Science

Sensor Technology and Measurement Systems
Research Guide

What is Sensor Technology and Measurement Systems?

Sensor Technology and Measurement Systems is a field focusing on the IEEE 1451 standard and its applications in networked smart transducer interfaces, encompassing smart sensors, resolver-to-digital conversion, capacitive sensors, neural networks, sensor networking, microcontroller interfaces, absolute encoders, and artificial neural networks.

This field includes 130,877 works with growth data unavailable over the past five years. It centers on standards like IEEE 1451 for integrating smart sensors into networks. Key areas involve signal processing techniques foundational to sensor data handling, as detailed in highly cited texts such as 'Discrete-Time Signal Processing' by Alan V. Oppenheim and Ronald W. Schafer (1989).

Topic Hierarchy

100%
graph TD D["Physical Sciences"] F["Computer Science"] S["Computer Networks and Communications"] T["Sensor Technology and Measurement Systems"] D --> F F --> S S --> T style T fill:#DC5238,stroke:#c4452e,stroke-width:2px
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130.9K
Papers
N/A
5yr Growth
508.5K
Total Citations

Research Sub-Topics

Why It Matters

Sensor Technology and Measurement Systems enables precise data acquisition in networked environments, supporting applications in IoT and smart cities. For instance, Agate Sensors raised €5.6 million in 2025 to commercialize miniaturized spectroscopy sensors, reducing equipment from suitcase-sized to compact forms for material analysis. Quantum sensing advances, such as QSENSATO's €500,000 funding for atomic sensors and NSF-backed projects for quantum-scale measurements, demonstrate impacts in ultra-precise metrology across industries. DARPA's $2 million grant to Mingyo Park's team funds force-sensing technologies using higher-order composites, highlighting defense applications.

Reading Guide

Where to Start

'Discrete-Time Signal Processing' by Alan V. Oppenheim and Ronald W. Schafer (1989) is the starting point for beginners, as it offers a thorough treatment of fundamental theorems and properties essential for understanding signal processing in sensor systems, with 11537 citations.

Key Papers Explained

'Discrete-Time Signal Processing' by Alan V. Oppenheim and Ronald W. Schafer (1989, 11537 citations) lays DSP foundations, which 'Applied Optimal Estimation' by A. Gelb (1974, 6388 citations) builds on for practical sensor data fusion. 'Modern Control Engineering' by K. Ogata and J. W. Brewer (1971, 6286 citations) extends this to control systems integrating sensors. 'Adaptive Signal Processing' (1991, 5121 citations) and 'Digital Signal Processing: Principles, Algorithms, and Applications' by J.G. Proakis and Dimitris G. Manolakis (1992, 4605 citations) advance adaptive methods for dynamic measurement environments.

Paper Timeline

100%
graph LR P0["The use of quarts oscillators fo...
1959 · 4.5K cites"] P1["Modern Control Engineering
1971 · 6.3K cites"] P2["Applied Optimal Estimation
1974 · 6.4K cites"] P3["Discrete-Time Signal Processing
1989 · 11.5K cites"] P4["Adaptive Signal Processing
1991 · 5.1K cites"] P5["Digital Signal Processing: Princ...
1992 · 4.6K cites"] P6["Discrete-time Signal Processing
1999 · 4.9K cites"] P0 --> P1 P1 --> P2 P2 --> P3 P3 --> P4 P4 --> P5 P5 --> P6 style P3 fill:#DC5238,stroke:#c4452e,stroke-width:2px
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Most-cited paper highlighted in red. Papers ordered chronologically.

Advanced Directions

Recent preprints like 'Sensor Arrays: A Comprehensive Systematic Review' (2025) analyze 361 papers on array patterns, while 'Future trends in sensor technology: A review' (2025) by Anurag Kumar Patel et al. targets IoT for smart cities. News highlights quantum sensors via NSF funding for 18 teams and DARPA's $2M grant for force-sensing composites.

Papers at a Glance

# Paper Year Venue Citations Open Access
1 Discrete-Time Signal Processing 1989 11.5K
2 Applied Optimal Estimation 1974 CERN Document Server (... 6.4K
3 Modern Control Engineering 1971 Journal of Dynamic Sys... 6.3K
4 Adaptive Signal Processing 1991 5.1K
5 Discrete-time Signal Processing 1999 Advanced textbooks in ... 4.9K
6 Digital Signal Processing: Principles, Algorithms, and Applica... 1992 4.6K
7 The use of quarts oscillators for weighing thin layers and for... 1959 European Physical Jour... 4.5K
8 Variation of peak temperature with heating rate in differentia... 1956 Journal of research of... 3.8K
9 Operation and modeling of the MOS transistor 1987 3.2K
10 Foundations of Time-Frequency Analysis 2001 Applied and numerical ... 2.9K

In the News

Code & Tools

Recent Preprints

Latest Developments

Recent developments in sensor technology and measurement systems include advancements in high-resolution hyperspectral imaging (Nature, 2024), the integration of quantum sensors for next-generation sensing applications (IEEE, 2025), and the ongoing expansion of the global sensor market projected to exceed US$500 billion within a decade (Nature, 2026). Additionally, emerging sensor technologies are focusing on reduced size and power consumption, increased sensitivity and accuracy, and the ability to measure more metrics (IDTechEx, ResearchAndMarkets, 2026).

Frequently Asked Questions

What is the IEEE 1451 standard?

The IEEE 1451 standard defines a networked smart transducer interface for integrating sensors into communication networks. It facilitates plug-and-play capabilities for smart sensors and actuators. This cluster of 130,877 papers emphasizes its role in sensor networking and microcontroller interfaces.

How do smart sensors function in measurement systems?

Smart sensors incorporate processing capabilities like neural networks for data handling in networked setups. They support resolver-to-digital conversion and absolute encoders per IEEE 1451. Papers like 'Discrete-Time Signal Processing' by Alan V. Oppenheim and Ronald W. Schafer (1989, 11537 citations) provide foundational signal processing methods.

What are key applications of capacitive sensors?

Capacitive sensors detect changes in capacitance for touch, proximity, and material sensing in transducer systems. They integrate with IEEE 1451 for networked operations. Recent trends include flexible tactile sensing systems for multifaceted structural designs.

What role do neural networks play in sensor technology?

Artificial neural networks process sensor data for tasks like pattern recognition in smart transducer interfaces. They enhance resolver-to-digital conversion and signal analysis. The field connects to related topics like Neural Networks Stability and Synchronization.

What is the current state of sensor arrays?

Sensor arrays arrange sensors in patterns like row-column distributions for enhanced measurement capabilities. A systematic review analyzed 361 papers over 10 years. They support IoT applications as reviewed in 'A Comprehensive Review of Sensor Technologies in IoT'.

How has optimal estimation advanced measurement systems?

Optimal estimation techniques, as in 'Applied Optimal Estimation' by A. Gelb (1974, 6388 citations), apply to sensor data fusion in engineering contexts. They emphasize practical implementations over pure mathematics. This supports control in systems like those in 'Modern Control Engineering' by K. Ogata and J. W. Brewer (1971, 6286 citations).

Open Research Questions

  • ? How can IEEE 1451 standards be extended for emerging quantum sensing networks?
  • ? What methods improve neural network stability in real-time sensor data processing?
  • ? Which integration techniques resolve synchronization challenges in distributed sensor arrays?
  • ? How do flexible materials advance tactile sensing for dynamic environments?
  • ? What are the limits of miniaturized spectroscopy in portable measurement systems?

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