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- Aqua and Terra MODIS (1)
- Artificial Intelligence (1)
- EPICS Global (1)
- EPICS North Africa (1)
- EneGate modular actuation control (1)
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- Frequency Hopping (1)
- Jamming (1)
- Landsat 7 8 and 9 (1)
- Maximum Length Sequences (1)
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- OptiGap sensor system in robotics (1)
- Pseudo Noise (1)
- Random Number Generators (1)
- SCRAMs in soft robotics (1)
- Sentinel 2A/2B (1)
- Shape-memory alloy in soft robotics (1)
- Soft robotics modular technologies (1)
- T2T cross-calibration (1)
- Thermally-activated SCRAM limb (TASL) technology (1)
Articles 1 - 4 of 4
Full-Text Articles in Engineering
A Map-Algebra-Inspired Approach For Interacting With Wireless Sensor Networks, Cyber-Physical Systems Or Internet Of Things, David Almeida
A Map-Algebra-Inspired Approach For Interacting With Wireless Sensor Networks, Cyber-Physical Systems Or Internet Of Things, David Almeida
Electronic Theses and Dissertations
The typical approach for consuming data from wireless sensor networks (WSN) and Internet of Things (IoT) has been to send data back to central servers for processing and analysis. This thesis develops an alternative strategy for processing and acting on data directly in the environment referred to as Active embedded Map Algebra (AeMA). Active refers to the near real time production of data, and embedded refers to the architecture of distributed embedded sensor nodes. Network macroprogramming, a style of programming adopted for wireless sensor networks and IoT, addresses the challenges of coordinating the behavior of multiple connected devices through a …
A Modular Framework For Surface-Embedded Actuation And Optical Sensing In Soft Robots., Paul Bupe Jr
A Modular Framework For Surface-Embedded Actuation And Optical Sensing In Soft Robots., Paul Bupe Jr
Electronic Theses and Dissertations
This dissertation explores the development and integration of modular technologies in soft robotics, with a focus on the OptiGap sensor system. OptiGap serves as a simple, flexible, cost-effective solution for real-time sensing of bending and deformation, validated through simulation and experimentation. Working as part of an emerging category of soft robotics called Soft, Curved, Reconfigurable, Anisotropic Mechanisms, or SCRAMs, this research also introduces the Thermally-Activated SCRAM Limb (TASL) technology, which employs shape-memory alloy (SMA) wire embedded in curved sheets for surface actuation and served as the initial inspiration for OptiGap. In addition, the EneGate system is presented as a complementary …
Validation Of Expanded Trend-To-Trend Cross-Calibration Technique And Its Application To Global Scale, Ramita Shah
Validation Of Expanded Trend-To-Trend Cross-Calibration Technique And Its Application To Global Scale, Ramita Shah
Electronic Theses and Dissertations
The expanded Trend-to-Trend (T2T) cross-calibration technique has the potential to calibrate two sensors in much less time and provides trends on daily assessment basis. The trend obtained from the expanded technique aids in evaluating the differences between satellite sensors. Therefore, this technique was validated with several trusted cross-calibration techniques to evaluate its accuracy. Initially, the expanded T2T technique was validated with three independent RadcaTS RRV, DIMITRI-PICS, and APICS models, and results show a 1% average difference with other models over all bands. Further, this technique was validated with other SDSU techniques to calibrate the newly launched satellite Landsat 9 with …
Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland
Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks, Charles Strickland
Electronic Theses and Dissertations
This work proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was initially optimized for use with a 12th order linear shift feedback register maximum length sequence utilizing a minimal polynomial as the characteristic polynomial. This neural network was then scaled to accommodate 7 different sequences, of orders 6 through 12. The neural network was trained for these sequences using training data that is 10 times the length of the sequence. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with …