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.

Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion

Abstract: We present a novel algorithm to produce descriptive online 3D occupancy maps using Gaussian processes (GPs). GP regression and classification have met with recent success in their application to robot mapping, as GPs are capable of expressing rich correlation among map cells and sensor data. However, the cubic computational complexity has limited its application to large-scale mapping and online use. In this paper we address this issue first by proposing test-data octrees, octrees within blocks of the map that prune away nodes of the same state, condensing the number of test data used in a regression, in addition to allowing fast data retrieval. We also propose a nested Bayesian committee machine which, after new sensor data is partitioned among several GP regressions, fuses the result and updates the map with greatly reduced complexity. Finally, by adjusting the range of influence of the training data and tuning a variance threshold implemented in our method's binary classification step, we are able to control the richness of inference achieved by GPs - and its tradeoff with classification accuracy. The performance of the proposed approach is evaluated with both simulated and real data, demonstrating that the method may serve both as an improved-accuracy classifier, and as a predictive tool to support autonomous navigation.

Heavy Metal Pollution and Remediation

Abstract: Although heavy metals are naturally occurring compounds, anthropogenic activities introduce them in excessive quantities in different environmental matrices, which impose severe threats on both human and ecosystem health. Heavy metals are nondegradable and can bioaccumulate in living organisms; hence they can contaminate the entire food chain. Remediation of heavy metals requires proper attention to protect soil quality, the ecosystem, and human health. Physical and chemical heavy metal remediation technologies are very expensive, often destructive to the local ecosystem, and require handling of a large amount of hazardous waste. On the other hand, emerging technologies such as phytoremediation have great potential. Phytoremediation is a “green” technology that is environment friendly and less expensive compared with other conventional methods. This chapter summarizes the problems related to heavy metal pollution and various heavy metal remediation technologies.

Tracking information flow in dynamic tree structures

Abstract: This paper explores the problem of tracking information flow in dynamic tree structures. Motivated by the problem of manipulating the Document Object Model (DOM) trees by browser-run client-side scripts, we address the dynamic nature of interactions via tree structures. We present a runtime enforcement mechanism that monitors this interaction and prevents a range of attacks, some of them missed by previous approaches, that exploit the tree structure in order to transfer sensitive information. We formalize our approach for a simple language with DOM-like tree operations and show that the monitor prevents scripts from disclosing secrets.