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.

Surface Diffusion of Poly(ethylene glycol)

Abstract: We report direct measurement of the center-of-mass diffusion coefficient, D, of uncharged flexible linear chains adsorbed at the solid−liquid interface at dilute surface coverage. We find D ∼ N-3/2 (N is degree of polymerization) when N was varied by more than an order of magnitude (N = 48, 113, 244, 456, and 693) and the scatter of the data was low. The experimental system was poly(ethylene glycol), PEG, adsorbed from dilute aqueous solution onto a self-assembled hydrophobic monolayer, condensed octadecyltriethoxysilane. The method of measurement was fluorescence correlation spectroscopy of a rhodamine green derivative dye that was end-attached to one sole end of the adsorbed PEG chains. The observed scaling implies the diffusion time τ ∼ N3 if Rg ∼ N3/4 as expected for a chain in good solvent in two dimensions (Rg is the radius of gyration), but a variety of other theoretical approaches to describe the dynamical scaling are also plausible. The multiplicity of plausible dynamical transport scenarios is c...

Understanding the color of human skin

Abstract: The automated detection of humans in computer vision as well as the realistic rendering of people in computer graphics necessitates a better understanding of human skin reflectance Prior vision and graphics research on this topic has primarily focused on images acquired with conventional color cameras. Although tri-color skin data is prevalent, it does not provide adequate information for explaining skin color or for discriminating between human skin and dyes designed to mimic human skin color. A better understanding of skin reflectance can be achieved through spectrographic analysis. Previous work in this field has largely been undertaken in the medical domain and focuses on the detection of pathology. Our work concentrates on the impact of skin reflectance on the image formation process. In our radiometric facility we measure the light reflected from the skin using a high resolution, high accuracy spectrograph under precisely calibrated illumination conditions. This paper presents observations from the first body of data gathered at this facility. From the measurements collected thus far, we have observed population-independent factors of skin reflectance. We show how these factors can be exploited in skin recognition. Finally, we provide a biological explanation for the existence of a distinguishing pattern in human skin reflectance.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Fine-Grained Abnormal Driving Behaviors Detection and Identification with Smartphones

Abstract: Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarse-grained result, i.e., distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a fine-grained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e., Weaving , Swerving , Sideslipping , Fast U-turn , Turning with a wide radius , and Sudden braking . Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a fine-grained abnormal D riving behavior D etection and i D entification system, $D^{3}$ , to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. We extract effective features to capture the patterns of abnormal driving behaviors. After that, two machine learning methods, Support Vector Machine (SVM) and Neuron Networks (NN), are employed, respectively, to train the features and output a classifier model which conducts fine-grained abnormal driving behaviors detection and identification. From results of extensive experiments with 20 volunteers driving for another four months in real driving environments, we show that $D^{3}$ achieves an average total accuracy of 95.36 percent with SVM classifier model, and 96.88 percent with NN classifier model.

Efficient Boosted Exemplar-Based Face Detection

Abstract: Despite the fact that face detection has been studied intensively over the past several decades, the problem is still not completely solved. Challenging conditions, such as extreme pose, lighting, and occlusion, have historically hampered traditional, model-based methods. In contrast, exemplar-based face detection has been shown to be effective, even under these challenging conditions, primarily because a large exemplar database is leveraged to cover all possible visual variations. However, relying heavily on a large exemplar database to deal with the face appearance variations makes the detector impractical due to the high space and time complexity. We construct an efficient boosted exemplar-based face detector which overcomes the defect of the previous work by being faster, more memory efficient, and more accurate. In our method, exemplars as weak detectors are discriminatively trained and selectively assembled in the boosting framework which largely reduces the number of required exemplars. Notably, we propose to include non-face images as negative exemplars to actively suppress false detections to further improve the detection accuracy. We verify our approach over two public face detection benchmarks and one personal photo album, and achieve significant improvement over the state-of-the-art algorithms in terms of both accuracy and efficiency.