Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Computer science & Cognitive radio. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Noneluting Enzymatic Antibiofilm Coatings

Abstract: We developed a highly efficient, biocompatible surface coating that disperses bacterial biofilms through enzymatic cleavage of the extracellular biofilm matrix. The coating was fabricated by binding the naturally existing enzyme dispersin B (DspB) to surface-attached polymer matrices constructed via a layer-by-layer (LbL) deposition technique. LbL matrices were assembled through electrostatic interactions of poly(allylamine hydrochloride) (PAH) and poly(methacrylic acid) (PMAA), followed by chemical cross-linking with glutaraldehyde and pH-triggered removal of PMAA, producing a stable PAH hydrogel matrix used for DspB loading. The amount of DspB loaded increased linearly with the number of PAH layers in surface hydrogels. DspB was retained within these coatings in the pH range from 4 to 7.5. DspB-loaded coatings inhibited biofilm formation by two clinical strains of Staphylococcus epidermidis. Biofilm inhibition was ≥98% compared to mock-loaded coatings as determined by CFU enumeration. In addition, DspB-loaded coatings did not inhibit attachment or growth of cultured human osteoblast cells. We suggest that the use of DspB-loaded multilayer coatings presents a promising method for creating biocompatible surfaces with high antibiofilm efficiency, especially when combined with conventional antimicrobial treatment of dispersed bacteria.

Empirical Evaluation of the Limits on Localization Using Signal Strength

Abstract: This work investigates the lower bounds of wireless localization accuracy using signal strength on commodity hardware. Our work relies on trace-driven analysis using an extensive indoor experimental infrastructure. First, we report the best experimental accuracy, twice the best prior reported accuracy for any localization system. We experimentally show that adding more and more resources (e.g., training points or landmarks) beyond a certain limit, can degrade the localization performance for lateration-based algorithms, and that it could only be improved further by "cleaning" the data. However, matching algorithms are more robust to poor quality RSS measurements. We next compare with a theoretical lower bound using standard Cramer Rao Bound (CRB) analysis for unbiased estimators, which is frequently used to provide bounds on localization precision. Because many localization algorithms are based on different mathematical foundations, we apply a diverse set of existing algorithms to our packet traces and found that the variance of the localization errors from these algorithms are smaller than the variance bound established by the CRB. Finally, we found that there exists a wide discrepancy from what freespace models predict in the signal to distance function even in an environment with limited shadowing and multipath, thereby imposing a fundamental limit on the achievable localization accuracy indoors.