Demand of multi-resolution over 8-bit is increasing remarkably in ultra-fast acquisition system. However, traditional solutions are either working with constraints or hardly to implement. In this paper, a reconfigurable deployment strategy based on time-synchronized (TS) sampling technique is proposed to offer a multi-resolution structure for ultra-fast acquisition system. In this system, multiple analog-to-digital converters (ADCs) could be rearranged under each specific condition to make full use of the hardware resources and balance sampling rate and resolution. In addition, the inherent resolution of ADC is broken through different combination for different resolution application. The simulation results validate that the proposed structure has multi-resolution (i.e., 8, 9, 10 and 11-bit) function at ultra-high sampling rate, moreover, the proposed method provide a powerful design reference for higher resolution acquisition systems.

We present a novel sensor system for the support of nutrition monitoring. The system is based on smart table cloth equipped with a fine grained pressure textile matrix and a weight sensitive tablet. Unlike many other nutrition monitoring approaches, our system is unobtrusive, non privacy invasive and easily deployable in every day life. It allows the spotting and recognition of food intake related actions, such as cutting, scooping, stirring, etc., the identification of the plate/container on which the action is executed, and the tracking of the weight change in the containers. In other words, we can determine how many pieces are cut on the main dish plate, how many are taken from the side dish, how many sips are taken from the drink, how fast the food is being consumed and how much weight is taken overall. In addition, the distinction between different eating actions, such as cutting, scooping, poking, provides clues to the type of food taken and the way the meal is consumed. We have evaluated our system on 40 meals (5 subjects) in a real life living environment: for seven eating related actions (cutting, scooping, stirring, etc.), resulting in above 90% average recognition rate for person dependent cases, and spotting each action out of continuous data streams (average F1 score 87%).