The quantitative evaluation of plant organs in a non-destructive and continuous fashion is the technological bottleneck to meet the food, fuel, and fiber needs for the 10 billion people on earth by 2050. Quantifying crop root architecture paves promising ways to improve resource uptake in the face of the resource limitations in the degraded soils of future climates. Current root measurement methods either have low resolution or involve uprooting the plant. In all cases, the measurement methods do not provide any prediction on how well the plant is growing. We propose the usage of three fiber Bragg gratings (FBG) embedded within soil to measure underground strain change due to pseudo-root growth and a Residual Neural Network (ResNet) to predict its characteristics in a non-destructive fashion. To generate large amounts of sensor data similar to that of a growing root, we developed an automated robot that inserts pseudo-roots of 1mm and 5mm in diameter to 15cm below the soil’s surface over the span of 11 minutes. We used 2,582 and 240 samples in training of the diameter and depth models, while testing was performed using 646 and 60 samples. The models were able to achieve accuracy of 92% and 93% for diameter and depth prediction, respectively. Through transfer learning, our base models will be expanded so that real time prediction on actual plant roots diameter and depth can be achieved.
Machine learning is a critical tool for sensing due to its ability to process and interpret complex sensor data, as well as to enhance the accuracy and efficiency of sensing applications in diverse fields. This paper provides an overview of machine learning’s multifaceted applications in microwave photonics, soft robotics, and precision agriculture sensing. Recently, machine learning techniques have revolutionized the field of microwave photonics. As an example, we will discuss an implementation of deep learning and generative adversarial network for data argumentation in instantaneous frequency measurement, which effectively decreases required training experimental dataset size by 98.75% and reduces error to <5%. Enhancing the practicability and accuracy of the system. Next, we shift our focus to the integration of fiber optic sensors in soft robotics to offer a lightweight, compact, and soft means of analyzing important robot parameters. By utilizing sensor data, machine learning algorithms enable real-time feedback, adaptability, and improved control of soft robot. Lastly, we also developed fiber optic sensors for non-invasive and continuous underground monitoring of root growth. Monitoring plant root growth is essential for agriculture; however, strain generated by the growth of root is relatively weak and noisy. Therefore, data collected by these fiber sensors is fed to a residual neural network to facilitate extraction of meaningful insights. In summary, machine learning has driven substantial progress in various fields that elevates the levels of accuracy and efficiency beyond previous achievements.
Optical technologies can be found in many aspects of our daily lives. We have developed three experiments using items that can be found in a toy box or around the house to demonstrate and explain optical concepts. Videos of the experiments, their principle, and applications are available on our YouTube channel. The first experiment uses moldable putty to make a shapeable lens and light guide to observe refraction and total internal reflection respectively. The putty’s characteristics allows for hand molding into different shapes to observe how light propagation is changed within the putty in real time. The second experiment is to learn about color absorption and reflection. The color pattern of clothing would change depending on the color of light that is used to illuminate it. The experiment illustrates how optical communication can use different colors to support multiple users. The third experiment uses a bubble to illustrate light interference, the principle behind eyeglasses coating. Different colors are seen at different locations on the bubble due to light interference. The above experiments can be carried out at home or at school through our outreach program. During our school outreach, we relate the above hands-on experiments with two demonstrations. The first demonstration is a laser-transmitted audio system that explains how electrical signals can be transmitted using optical fibers. While the second allows for the observation of how laser light is guided within an optical fiber. The toy-based experiments are a fun approach to introduce complex concepts to students.
Emerging RF systems utilize multiple frequency bands to facilitate multi-function operations and to adapt to dynamic transmission conditions, making multiband RF systems an essential infrastructure for applications in the commercial, defense, and civilian federal marketplace. While multiband RF systems are the backbone for intelligence, surveillance, and reconnaissance, as well as for supporting data-intensive physical weaponry in the battlefield; Civilians also rely on multiband RF systems for all types of day-to-day applications including smart home system control, entertainment, virtual reality and augmented reality learning. With the recent development of 5G networks, the spectrum of multiband networks could spend from hundreds of MHz to tens of GHz range, which could support new applications and improve the quality of services. The benefits associated with using multiband and wideband RF technologies can only be realized if it is possible to dynamically manipulate the ultra-wide multiband spectrum to ensure high-quality transmission performance. This is challenging, however, as the bandwidth of multiband RF signal could be as wide as several GHz with a center frequency from hundreds of MHz to tens of GHz range, and neither RF electronics nor digital signal processing are capable of dynamically manipulating spectrum of GHz wide. In this paper, we will present our recent advancement on novel photonic systems for dynamically manipulating the wide RF spectrum for multiband and wideband emerging RF systems.
Spike processing is one kind of hybrid analog-digital signal processing, which has the efficiency of analog processing
and the robustness to noise of digital processing. When instantiated with optics, a hybrid analog-digital processing
primitive has the potential to be scalable, computationally powerful, and have high operation bandwidth. These devices
open up a range of processing applications for which electronic processing is too slow. Our approach is based on a
hybrid analog/digital computational primitive that elegantly implements the functionality of an integrate-and-fire neuron
using a Ge-doped non-linear optical fiber and off-the-shelf semiconductor devices. In this paper, we introduce our
photonic neuron architecture and demonstrate the feasibility of implementing simple photonic neuromorphic circuits,
including the auditory localization algorithm of the barn owl, which is useful for LIDAR localization, and the crayfish
tail-flip escape response.
Using optical processing techniques, we experimentally enhance the physical layer security of optical communication
systems. We exploit optical encryption using fiber nonlinearity to achieve real time data encryption. By implementing
interleaved waveband switching modulation and variable two-code keying to the system, the security of the data is
further enhanced. Based on spread spectrum, we also demonstrate optical steganography such that the stealth signal is
transmitted underneath system noise. Optical steganography in WDM and optical CDMA systems is experimentally
demonstrated. We also propose and study optical CDMA-based backup channels that improve service availability
without wasting the bandwidth in the backup channel. The multi-layered security provided improves the confidentiality
and availability of the network.
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