Testing Microfluidic Fully Programmable Valve Arrays (FPVAs)

Fully Programmable Valve Array (FPVA) has emerged as a new architecture for the next-generation flow-based microfluidic biochips. This 2D-array consists of regularly-arranged valves, which can be dynamically configured by users to realize microfluidic devices of different shapes and sizes as well as interconnections. Additionally, the regularity of the underlying structure renders FPVAs easier to integrate on a tiny chip. However, these arrays may suffer from various manufacturing defects such as blockage and leakage in control and flow channels. Unfortunately, no efficient method is yet known for testing such a general-purpose architecture. In this paper, we present a novel formulation using the concept of flow paths and cut-sets, and describe an ILP-based hierarchical strategy for generating compact test sets that can detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of the proposed method in detecting manufacturing faults with only a small number of test vectors.Comment: Design, Automation and Test in Europe (DATE), March 201

An adsorbed gas estimation model for shale gas reservoirs via statistical learning

Shale gas plays an important role in reducing pollution and adjusting the structure of world energy. Gas content estimation is particularly significant in shale gas resource evaluation. There exist various estimation methods, such as first principle methods and empirical models. However, resource evaluation presents many challenges, especially the insufficient accuracy of existing models and the high cost resulting from time-consuming adsorption experiments. In this research, a low-cost and high-accuracy model based on geological parameters is constructed through statistical learning methods to estimate adsorbed shale gas conten

Reduced length checking sequences

Here, the method proposed by Ural, Wu and Zhang (1997) for constructing minimal-length checking sequences based on distinguishing sequences is improved. The improvement is based on optimizations of the state recognition sequences and their use in constructing test segments. It is shown that the proposed improvement further reduces the length of checking sequences produced from minimal, completely specified, and deterministic finite state machines

Distinguishing sequences for partially specified FSMs

Distinguishing Sequences (DSs) are used inmany Finite State Machine (FSM) based test techniques. Although Partially Specified FSMs (PSFSMs) generalise FSMs, the computational complexity of constructing Adaptive and Preset DSs (ADSs/PDSs) for PSFSMs has not been addressed. This paper shows that it is possible to check the existence of an ADS in polynomial time but the corresponding problem for PDSs is PSPACE-complete. We also report on the results of experiments with benchmarks and over 8 * 106 PSFSMs. © 2014 Springer International Publishing

Diffuse supernova neutrinos at underground laboratories

I review the physics of the Diffuse Supernova Neutrino flux (or Background, DSNB), in the context of future searches at the next generation of neutrino observatories. The theory of the DSNB is discussed in its fundamental elements, namely the cosmological rate of supernovae, neutrino production inside a core collapse supernova, redshift, and flavor oscillation effects. The current upper limits are also reviewed, and results are shown for the rates and energy distributions of the events expected at future liquid argon and liquid scintillator detectors of O(10) kt mass, and water Cherenkov detectors up to a 0.5 Mt mass. Perspectives are given on the significance of future observations of the DSNB, both at the discovery and precision phases, for the investigation of the physics of supernovae and of the properties of the neutrino.Comment: latex, 94 pages. 35 figures and 13 tables. Version extensively updated. Accepted in Astroparticle Physic