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: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Undermining Information Hiding (and What to Do about It)

Abstract: In the absence of hardware-supported segmentation, many state-of-the-art defenses resort to “hiding” sensitive information at a random location in a very large address space. This paper argues that information hiding is a weak isolation model and shows that attackers can find hidden information, such as CPI’s SafeStacks, in seconds—by means of thread spraying. Thread spraying is a novel attack technique which forces the victim program to allocate many hidden areas. As a result, the attacker has a much better chance to locate these areas and compromise the defense. We demonstrate the technique by means of attacks on Firefox, Chrome, and MySQL. In addition, we found that it is hard to remove all sensitive information (such as pointers to the hidden region) from a program and show how residual sensitive information allows attackers to bypass defenses completely. We also show how we can harden information hiding techniques by means of an Authenticating Page Mapper (APM) which builds on a user-level page-fault handler to authenticate arbitrary memory reads/writes in the virtual address space. APM bootstraps protected applications with a minimum-sized safe area. Every time the program accesses this area, APM authenticates the access operation, and, if legitimate, expands the area on demand. We demonstrate that APM hardens information hiding significantly while increasing the overhead, on average, 0.3% on baseline SPEC CPU 2006, 0.0% on SPEC with SafeStack and 1.4% on SPEC with CPI.

Microwave-induced organic reaction enhancement (more) chemistry: Techniques for rapid, safe and inexpensive synthesis

Abstract: Synthetic organic reactions have been conducted under microwave irradiation in open vessels in unaltered domestic microwave ovens. Reaction times vary from a few seconds for sub-milligram reactions to about 15 minutes for reactions carried out on a scale of hundreds of grams. Promising results have been obtained for several condensations, as well as the Bischler-Napieralski reaction, the Wolff-Kishner reduction, free radical dehalogenation reactions, and other standard synthetic operations. Rapid catalytic transfer hydrogenation using ammonium formate as the source of hydrogen has been conducted at about 100-130 °C under microwave irradiation.

Method and apparatus for dynamic spectrum access

Abstract: Methods and apparatus are provided for the efficient use of cognitive radios in the performance of dynamic spectrum access. This includes spectrum sensing algorithms, adaptive optimized sensing of parameters based on changing radio environments, and identifying vacant channels. Protocols for switching communication links to different channels, and synchronizing communicating nodes in order to maintain reliable connectively while maximizing spectrum usage are also provided.

Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification.

Abstract: Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30 Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n = 387), and over 90% sensitivity and precision on an independent youth dataset (n = 32). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.