2 2 ExperimentsPure algae samples were grown at the Center for

2.2. ExperimentsPure algae samples were grown at the Center for Coastal Studies laboratories in f/2 media and included the eustimatophyte Nannochloropsis salina (nanno), the diatom Phaeodactylum tricornutum (phaeo), and unidentified coccoid cyanobacteria, which represent members of the green, brown, and cyanobacterial plant line of algae. The samples varied in algae density based on growth parameters and environmental factors. The algae samples were shaken gently before hyperspectral analyses to prevent algae from settling at the bottom of the tubes or forming aggregates that could affect hyperspectral scans. Care was taken to prepare a homogenous-looking batch for experimental measurements.Two independent set of experiments were conducted to test the hyperspectral imaging system’s performance.

The first set of experiments investigated spectral composition of two algal species in their pure and mixed forms. Each measurement was taken from a fixed volume of 10 mL. Spectra from pure algae (100%) and algae mixed in preset ratios (10%�C90%, 50%�C50%, 90%�C10% combinations) were acquired and used in the constraint linear spectral unmixing model as discussed in Section 3.1 to determine the percent algae composition of the tested mixtures. Spectra from algal suspensions of 100% single-species were used as reference spectra.The second set of experiments assessed the hyperspectral imaging system’s as well as the linear spectral unmixing model’s ability to differentiate among various mixed volumes of pure algae suspe
Autonomous robotic systems function well in a carefully defined workspace.

However, assistive devices such as robotic wheelchairs need to consider user requirements whilst negotiating highly dynamic and varied arenas, particularly Dacomitinib as indoor activity is highly room correlated. Thus, for any effective assistive system a robust degree of real-time localization becomes essential. Obtaining and maintaining online coarse self-localization would allow assistive systems to select appropriate navigation strategies such as when approaching doorways and waypoints or following corridors, and to know precisely when room boundaries are crossed; more importantly maintaining coarse localization allows the system and human to converse using the exact same terms and to communicate that information to other automated systems or human assistants.

Localization can be achieved using Global Positioning Satellites (GPS) or mobile telephony techniques. However, the degree of accuracy and loss of signal can present a real problem within buildings, particularly when there is a need to differentiate between small rooms as is common in domestic situations. Tracking and localization within a room has been covered extensively within the literature [1,2]. While current research favors optical methods [3], Wi-Fi systems are however widely employed and considered by many a de facto standard method [4].

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