Source localization and spectral estimation are among the most fundamental problems in statistical and array signal processing. Methods which rely on the orthogonality of the signal and noise subspaces, such as Pisarenko's method, MUSIC, and root-MUSIC are some of the most widely used algorithms to solve these problems. As a common feature, these methods require both apriori knowledge of the number of sources, and an estimate of the noise subspace. Both requirements are complicating factors to the practical implementation of the algorithms, and when not satisfied exactly, can potentially lead to severe errors. In this paper, we propose a new localization criterion based on the algebraic structure of the noise subspace that is described for the first time to the best of our knowledge. Using this criterion and the relationship between the source localization problem and the problem of computing the greatest common divisor (GCD), or more practically approximate GCD, for polynomials, we propose two algorithms which adaptively learn the number of sources and estimate their locations. Simulation results show a significant improvement over root-MUSIC in challenging scenarios such as closely located sources, both in terms of detection of the number of sources and their localization over a broad and practical range of SNRs. Further, no performance sacrifice in simple scenarios is observed.

Closed-cell foams are widely applied as insulation and essential for the thermal management of protective garments for extreme environments. In this work, we develop and demonstrate a strategy for drastically reducing the thermal conductivity of a flexible, closed-cell polychloroprene foam to 0.031 ± 0.002 W m−1 K−1, approaching values of an air gap (0.027 W m−1 K−1) for an extended period of time (>10 hours), within a material capable of textile processing. Ultra-insulating neoprene materials are synthesized using high-pressure processing at 243 kPa in a high-molecular-weight gas environment, such as Ar, Kr, or Xe. A Fickian diffusion model describes both the mass infusion and thermal conductivity reduction of the foam as a function of processing time, predicting a 24–72 hour required exposure time for full charging of a 6 mm thick 5 cm diameter neoprene sample. These results enable waterproof textile insulation that approximates a wearable air gap. We demonstrate a wetsuit made of ultra-low thermally conductive neoprene capable of potentially extending dive times to 2–3 hours in water below 10 °C, compared with <1 hour for the state-of-the-art. This work introduces the prospect of effectively wearing a flexible air gap for thermal protection in harsh environments.

In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub-spaces, we show how deterministic samplers can be designed under a Neyman-Pearson-like setting when the generalized likelihood ratio test is used. For a less stringent case than the worst case design, we introduce a submodular cost that obtains comparable results with its convex counterpart, while having a linear time heuristic for its near optimal maximization.

Materials science has made progress in maximizing or minimizing the thermal conductivity of materials; however, the thermal effusivity—related to the product of conductivity and capacity—has received limited attention, despite its importance in the coupling of thermal energy to the environment. Herein, we design materials that maximize the thermal effusivity by impregnating copper and nickel foams with conformal, chemical-vapor-deposited graphene and octadecane as a phase change material. These materials are ideal for ambient energy harvesting in the form of what we call thermal resonators to generate persistent electrical power from thermal fluctuations over large ranges of frequencies. Theory and experiment demonstrate that the harvestable power for these devices is proportional to the thermal effusivity of the dominant thermal mass. To illustrate, we measure persistent energy harvesting from diurnal frequencies, extracting as high as 350 mV and 1.3 mW from approximately 10 °C diurnal temperature differences.

Thermal diodes are materials that allow for the preferential directional transport of heat and are highly promising devices for energy conservation, energy harvesting, and information processing applications. One form of a thermal diode consists of the junction between a phase change and phase invariant material, with rectification ratios that scale with the square root of the ratio of thermal conductivities of the two phases. In this work, the authors introduce and analyse the concept of a Dual Phase Change Thermal Diode (DPCTD) as the junction of two phase change materials with similar phase boundary temperatures but opposite temperature coefficients of thermal conductivity. Such systems possess a significantly enhanced optimal scaling of the rectification ratio as the square root of the product of the thermal conductivity ratios. Furthermore, the authors experimentally design and fabricate an ambient DPCTD enabled by the junction of an octadecane-impregnated polystyrene foam, polymerized using a high internal phase emulsion template (PFH-O) and a poly(N-isopropylacrylamide) (PNIPAM) aqueous solution. The DPCTD shows a significantly enhanced thermal rectification ratio both experimentally (2.6) and theoretically (2.6) as compared with ideal thermal diodes composed only of the constituent materials.