A reactive jammer jams wireless channels only when target devices are transmitting; Compared to constant jamming, reactive jamming is harder to track and compensate against [2], [42]. Frequency Hopping Spread Spectrum (FHSS) and Direct Sequence Spread Spectrum (DSSS) have been widely used as countermeasures against jamming attacks. However, both will fail if the jammer jams all frequency channels or has high transmit power. In this paper, we propose an anti-jamming communication system that allows communication in the presence of a broadband and high power reactive jammer. The proposed system transmits messages by harnessing the reaction time of a reactive jammer. It does not assume a reactive jammer with limited spectrum coverage and transmit power, and thus can be used in scenarios where traditional approaches fail. We develop a prototype of the proposed system using GNURadio. Our experimental evaluation shows that when a powerful reactive jammer is presence, the prototype still keeps communication, whereas other schemes such as 802.11 DSSS fail completely.

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Environmental factors such as temperature, nutrients, and pollutants affect the growth rates and physical characteristics of microalgae populations. As algae play a vital role in marine ecosystems, the monitoring of algae is important to observe the state of an ecosystem. However, analyzing these microalgae populations using conventional light microscopy is time-consuming and requires experts to both identify and count the algal cells, which in turn considerably limits the volume of the samples that can be measured in each experiment. In this work we use a high-throughput and field-portable imaging flow cytometer to perform automated labelfree phenotypic analysis of marine microalgae populations using image processing and deep learning. The imaging flow cytometer provides color intensity and phase images of microalgae contained in a liquid sample by capturing and reconstructing the lens-free color holograms of the continuously flowing liquid at a flow rate of 100 mL/h. We extracted the spatial and spectral features of each algal cell in a sample from these holographic images and performed automated algae identification using convolutional neural networks. These features, alongside the composition and growth rate of the algae within the samples, were analyzed to understand the interactions between different algae populations as well as the effects of toxin exposure. As proof of concept, we demonstrated the effectiveness of the system by analyzing the impact of various concentrations of copper on microalgae monocultures and mixtures.

We report a field-portable and cost-effective imaging flow cytometer that uses deep learning to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL/h. This...