This paper describes torque-actuated valves for controlling the flow of fluids in microfluidic channels. The valves consist of small machine screws (> or =500 microm) embedded in a layer of polyurethane cast above microfluidic channels fabricated in poly(dimethylsiloxane) (PDMS). The polyurethane is cured photochemically with the screws in place; on curing, it bonds to the surrounding layer of PDMS and forms a stiff layer that retains an impression of the threads of the screws. The valves were separated from the ceiling of microfluidic channels by a layer of PDMS and were integrated into channels using a simple procedure compatible with soft lithography and rapid prototyping. Turning the screws actuated the valves by collapsing the PDMS layer between the valve and channel, controlling the flow of fluids in the underlying channels. These valves have the useful characteristic that they do not require power to retain their setting (on/off). They also allow settings between "on" and "off" and can be integrated into portable, disposable microfluidic devices for carrying out sandwich immunoassays.

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Infrared imagery over long ranges is hampered by atmospheric turbulence effects, leading to spatial resolutions worse than expected by a diffraction limited sensor system. This diminishes the recognition range and it is therefore important to compensate visual degradation due to atmospheric turbulence. The amount of turbulence is spatially varying due to anisoplanatic conditions, while the isoplanatic angle varies with atmospheric conditions. But also the amount of turbulence varies significantly in time. In this paper a method is proposed that performs turbulence compensation using a patch-based approach. In each patch the turbulence is considered to be approximately spatially and temporally constant. Our method utilizes multi-frame super-resolution, which incorporates local registration, fusion and deconvolution of the data and also can increase the resolution. This makes our method especially suited to use under anisoplanatic conditions. In our paper we show that our method is capable of compensating the effects of mild to strong turbulence conditions.

Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visualintelligence systems are desired. In this paper, we present an improved system that can recognize actions of a human and interactions between multiple humans. Central to the new system is our agent-based architecture. The system is trained on thousands of videos and evaluated on realistic persistent surveillance data in the DARPA Mind's Eye program, with hours of videos of challenging scenes. The results show that our system is able to track the people, detect and localize events, and discriminate between different behaviors, and it performs 3.4 times better than our previous system.