This article presents the initial development of a biomass sensor to monitor the growth of macroalgae (seaweeds) in the Integrated Multi-Trophic Aquaculture (IMTA) at Harbor Branch Oceanographic Institute at Florida Atlantic University. The sensor utilizes a combined optical/acoustic means to quantify the seaweed biomass. Such configuration ensures the sensor providing robust coverage under different ambient conditions and biomass densities. After the biomass sensor’s performance has been validated in the lab environment, we deployed the sensor at the macroalgal seaweed cultivation raceway to quantify the seaweed density. The data processing procedures are documented, and the field test results are presented. Finally, the advantages and disadvantages of this approach and future application of the sensor to drive a machine learning-based prediction biomass model are discussed.
Hobbyist electronics have greatly improved in both quality and capability over the past several years. It is now possible to solve computationally challenging problems with equipment costing less than a few hundred US Dollars. Both of the datalogger concepts presented in this work leverage this improvement by using off-the-shelf technology to replace what could previously only be done with expensive custom hardware. The processors used in these concepts, the Teensy 4.0 Audio Adapter and the CTAG BEAST, were originally designed for musicians who require the ability to manipulate multiple channels of audio simultaneously. This capability, however, also enables the construction of dataloggers capable of recording perfectly synchronized multi-channel audio – a requirement for passive phased sonar arrays. Each datalogger carries the additional benefit of low power consumption, permitting the array to be deployed for several hours before requiring recovery. Future versions of the dataloggers are expected to have mission durations comparable to existing commercial systems. This paper follows the development process of both of these concepts and compares each of their performance. The first concept, the Teensy, consists of two Teensy 4.0 control boards, each sandwiched between two Teensy Audio Adapters. This assembly is capable of recording up to eight channels of audio in near perfect sync. The second concept, the BEAST, consists of a BeagleBone Black single board computer augmented with a CTAG BEAST cape. This system is capable of recording eight channels of perfectly synchronized audio. Both of these systems were tested in the field with a four-element co-prime sonar array. The data is analyzed and the results of the comparison are presented in this work. Finally, some operational recommendations and possibilities for improvement are also discussed.
We present a near-field source localization method for use with a recently proposed inflatable array-based sonar system. The sensing system, known as the Underwater Inflatable Co-prime Sonar Array, can be easily deployed from an unmanned underwater vehicle (UUV), as it can be stored as a folded package with compact stowed dimension. This package can be detached from the UUV and it can morph into its final structural form at the destination. The co-prime sparse array configuration offers the capability to resolve a much higher number of sources as compared to a conventional uniform half-wavelength-spaced array. Using real-data measurements in a water tank with a co-prime array prototype, we demonstrate the capability of the proposed system to localize wideband acoustic sources.
Unobtrusive human movement monitoring is important in elderly care and assisted living to alert the caregivers of any potential accidents. The current state of art relies on features extracted from a radar system. The conventional optical camera system is not generally used in this application due to the concern of privacy. As an alternative, we investigate a different kind of electro-optical sensor – compressive active sensing electro-optical (CASEO) sensor. A CSAEO system consists of an infrared (IR) based spatial light modulation devices (SLM) based illuminator and a single-element avalanche photodetector (APD) based receiver. The scene information is encoded through a sequence of coded illumination patterns via the SLM illuminator and the corresponding measurements captured by the APD. Such compressive measurements can be used in scene classification instead of the actual video frames. In this work, we explore the feasibility of using CASEO in the identification of different activities. A CASEO processing framework was developed. Simulations using UIUC Human Activity Recognition test dataset were conducted to validate this framework.
In many space-borne surveillance missions, hyperspectral imaging (HSI) sensors are essential to enhance the ability to analyze and classify oceanic and terrestrial parameters and objects/areas of interest. A significant technical challenge is that the amount of raw data acquired by these sensors will begin to exceed the data transmission bandwidths between the spacecraft and the ground station using classical approaches such as imaging onto a detector array. To address such an issue, the compressive line sensing (CLS) imaging concept, originally developed for energy-efficient active laser imaging, is adopted in the design of a hyperspectral imaging sensor. CLS HSI imaging is achieved using a digital micromirror device (DMD) spatial light modulator. A DMD generates a series of 2D binary sensing patterns from a codebook that can be used to encode cross-track spatial-spectral slices in a push-broom type imaging device. In this paper, the development of a testbed using the TI DLP NIRscan™ Nano Evaluation Module to investigate the CLS HSI concept is presented. Initial test results are discussed.
In situ detection, tracking, localization and identification of undersea marine life in their natural environment is an important aspect of marine biology, fisheries management, ecology and environmental impact studies in the vicinity of undersea infrastructure. However due to the challenging optical characteristics of the underwater environment, mainly due to attenuation and scattering, it is not operationally effective to observe marine life using conventional approaches, such as underwater cameras and lights operating in the visible spectrum. Images often appear dim and blurry and increasing the photon output of the flood lamps or strobes does not solve the issue, instead leading to the formation of image hotspots, and in turbid conditions also reducing image contrast and resolution due to increased back-scattering and blur/glow field effects due to increased forward-scattering. Perhaps more importantly, the introduction of bright broadband lighting into the underwater environment is known to induce behavioral changes in the animals being studied. The MEMS-based serial LiDAR (Light Detection and Ranging) detection and imaging system that was recently developed uses red (638 nm) pulsed laser diode illumination to be invisible and eye-safe to marine animals. Furthermore it has the potential to be very compact, and cost-effective. The equipment is designed for long-term, maintenance-free operations. It generates a sparse primary dataset that only includes detected anomalies, with dense identification-quality dataset being triggered within a scan cycle, thus allowing for efficient, real-time, automated, low bandwidth animal detection, classification and identification. This paper outlines the operating principles of the detection and imaging optical and electronic architecture, with an example of recent results obtain in turbid coastal conditions.
Passive hyperspectral imaging (HSI) sensors are essential in many space-borne surveillance missions because rich spectral information can improve the ability to analyze and classify oceanic and terrestrial parameters and objects/areas of interest. A significant technical challenge is that the amount of raw data acquired by these sensors will begin to exceed the data transmission bandwidths between the spacecraft and the ground station using classical approaches such as imaging onto a detector array. In this paper, the Compressive Line Sensing (CLS) imaging concept, originally developed for energy-efficient active laser imaging, is extended to the implementation of a hyperspectral imaging sensor. CLS HSI imaging is achieved using a digital micromirror device (DMD) spatial light modulator. A DMD generates a series of 2D binary sensing patterns from a codebook that can be used to encode cross-track spatial-spectral slices in a push-broom type imaging device. A high sensitivity single-element detector can then be used to acquire the target reflections from the DMD as the encoder output. The target image can be reconstructed using the encoder output and the encoding codebook. The proposed system architecture is presented. The initial simulation and experimental results comparing the proposed design with the state-of-the-art are discussed.
In many scientific and defense surveillance missions, reducing the size, weight, and power (SWaP) of sensing systems is critical to accomplishing the intended objectives. At the backend, compressive sensing (CS) has been widely adopted to maintain the signal fidelity with less measurements, thereby reducing the hardware complexity. On the other hand, SWaP reduction can also be achieved with intelligent mechanical design. In this paper, we discuss a novel system concept, namely, Underwater Inflatable Co-prime Sonar Array (UICSA), which provides SWaP compression on two fronts. First, the sonar array is implemented as an inflatable structure, also referred to as a deployable structure, which is a folded package with compact stowed dimension. The folded package can be detached from a carrying platform and it can morph into its final structure form at the destination. Second, a sparse array configuration, namely, a co-prime array, is employed, which can resolve a much higher number of sources compared to a conventional uniform half-wavelength spaced array for a given number of sensors. The integration of these two concepts leads to a simulatenous reduction in the stowed dimension of the sonar array and the number of employed hydrophones. We describe the development of a UICSA prototype and provide underwater source direction-of-arrival estimates obtained using initial datasets acquired with the developed prototype.
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