We present a real-time target-locking confocal microscope that follows an object moving along an arbitrary path, even as it simultaneously changes its shape, size and orientation. This Target-locking Acquisition with Realtime Confocal (TARC) microscopy system integrates fast image processing and rapid image acquisition using a Nipkow spinning-disk confocal microscope. The system acquires a 3D stack of images, performs a full structural analysis to locate a feature of interest, moves the sample in response, and then collects the next 3D image stack. In this way, data collection is dynamically adjusted to keep a moving object centered in the field of view. We demonstrate the system's capabilities by target-locking freely-diffusing clusters of attractive colloidal particles, and activelytransported quantum dots (QDs) endocytosed into live cells free to move in three dimensions, for several hours. During this time, both the colloidal clusters and live cells move distances several times the length of the imaging volume.

We investigate the 3D structure and drying dynamics of complex mixtures of emulsion droplets and colloidal particles, using confocal microscopy. Air invades and rapidly collapses large emulsion droplets, forcing their contents into the surrounding porous particle pack at a rate proportional to the square of the droplet radius. By contrast, small droplets do not collapse, but remain intact and are merely deformed. A simple model coupling the Laplace pressure to Darcy's law correctly estimates both the threshold radius separating these two behaviors, and the rate of large-droplet evacuation. Finally, we use these systems to make novel hierarchical structures. The drying of suspensions of colloidal particles gives rise a plethora of fascinating phenomena, from the "coffee-ring" effect[1] to episodic crack propogation [2] and the fractal patterns arising from invasion percolation [3][4][5][6][7]. Drying of colloidal suspensions is also important technologically: paints and other coatings depend on colloidal particles for many of their key properties, many ceramics go through a stage of particle drying, and cosmetics often exploit the unique properties of colloidal-scale particles, particularly for such beneficial properties as screening the harmful effects of the sun. However, for many of these technological applications, the colloidal particles are but one of many different components, and drying of the colloids is accompanied by many other phase changes. While these mixtures can become highly complex, a simpler, yet still rich system that embodies many of the complex phenomena of these technological suspensions is a mixture of immiscible fluids with a colloidal suspension; a simple example is a mixture of an emulsion and colloidal particles. The behavior of the emulsion embodies many of the archetypal phenomena of such systems, while still remaining sufficiently tractable to enable it to be fully understood. However, emulsions themselves typically scatter light significantly, and when mixed with a colloidal suspension, this scattering is only enhanced. As a result, it is very difficult to image this mixture, precluding optical studies of its behavior, and knowledge of the actual behavior is woefully missing.In this Letter, we explore the drying of mixtures of aqueous emulsion droplets and spherical colloidal particles with confocal microscopy, which allows us to resolve the full 3D structure of these mixtures and their temporal dynamics. We find that the particles first jam into a solidified pack, throughout which emulsion drops are dispersed; a front of air then passes through the entire system. When this drying front reaches large emulsion droplets, the droplets unexpectedly collapse and their internal contents are forced into the pore space between the surrounding colloids, driven by an imbalance of pressures at the droplets' interfaces with air and with the solvent. By contrast, small droplets are deformed by the drying front, yet remain intact without bursting. By coupling the Laplace pressure with Darcy's la...

Over the past decade, mobile computing and wireless communication have become increasingly important drivers of many new computing applications. The field of wireless sensor networks particularly focuses on applications involving autonomous use of compute, sensing, and wireless communication devices for both scientific and commercial purposes. This paper examines the research decisions and design tradeoffs that arise when applying wireless peer-to-peer networking techniques in a mobile sensor network designed to support wildlife tracking for biology research.The ZebraNet system includes custom tracking collars (nodes) carried by animals under study across a large, wild area; the collars operate as a peer-to-peer network to deliver logged data back to researchers. The collars include global positioning system (GPS), Flash memory, wireless transceivers, and a small CPU; essentially each node is a small, wireless computing device. Since there is no cellular service or broadcast communication covering the region where animals are studied, ad hoc, peer-to-peer routing is needed. Although numerous ad hoc protocols exist, additional challenges arise because the researchers themselves are mobile and thus there is no fixed base station towards which to aim data. Overall, our goal is to use the least energy, storage, and other resources necessary to maintain a reliable system with a very high 'data homing' success rate. We plan to deploy a 30-node ZebraNet system at the Mpala Research Centre in central Kenya. More broadly, we believe that the domain-centric protocols and energy tradeoffs presented here for ZebraNet will have general applicability in other wireless azld sensor applications.

We implement image correlation, a fundamental component of many real-time imaging and tracking systems, on a graphics processing unit (GPU) using NVI-DIA's CUDA platform. We use our code to analyze images of liquid-gas phase separation in a model colloid-polymer system, photographed in the absence of gravity aboard the International Space Station (ISS). Our GPU code is 4,000 times faster than simple MATLAB code performing the same calculation on a central processing unit (CPU), 130 times faster than simple C code, and 30 times faster than optimized C?? code using single-instruction, multipledata (SIMD) extensions. The speed increases from these parallel algorithms enable us to analyze images downlinked from the ISS in a rapid fashion and send feedback to astronauts on orbit while the experiments are still being run.

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