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.

Programmable lock and security system therefor

Abstract: Locks (2) for the transportation industry are programmable with a keypad and with handheld activators (116, 118), the activators being programmable by a central system (134) and activators via IR transmitters and receivers. Operator PIN numbers and access codes manifesting the supervisory level of authority are encoded in each lock (2) which are programmed to open a given number of times in a given time period with or without entry of a code and include a lockout feature for disabling the lock in case of invalid code entry. Each lock has a lock history containing the number of incomplete opening transactions, when they occured and the operator codes associated therewith. The locks are opened by IR transmission of the appropriate codes or by keypad entries. One or more individuals at different levels of authority may open one or more locks in a given time frame a given number of times. Each lock records its transaction history which is displayed and downloaded for system evaluation.

Joint Position and Travel Path Optimization for Energy Efficient Wireless Data Gathering Using Unmanned Aerial Vehicles

Abstract: Unnamed aerial vehicles (UAVs) or drones have attracted growing interest in the last few years for multiple applications; thanks to their advantages in terms of mobility, easy movement, and flexible positioning. In UAV-based communications, mobility and higher line-of-sight probability represent opportunities for the flying UAVs while the limited battery capacity remains its major challenge. Thus, they can be employed for specific applications where their permanent presence is not mandatory. Data gathering from wireless sensor networks is one of these applications. This paper proposes an energy-efficient solution minimizing the UAV and/or sensors energy consumption while accomplishing a tour to collect data from the spatially distributed wireless sensors. The objective is to determine the positions of the UAV “stops” from which it can collect data from a subset of sensors located in the same neighborhood and find the path that the UAV should follow to complete its data gathering tour in an energy-efficient manner. A non-convex optimization problem is first formulated then, an efficient and low-complex technique is proposed to iteratively achieve a sub-optimal solution. The initial problem is decomposed into three sub-problems: The first sub-problem optimizes the positioning of the stops using linearization. The second one determines the sensors assignment to stops using clustering. Finally, the path among these stops is optimized using the travel salesman problem. Selected numerical results show the behavior of the UAV versus various system parameters and that the achieved energy is considerably reduced compared to the one of existing approaches.

Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation

Abstract: We propose an unsupervised detector adaptation algorithm to adapt any offline trained face detector to a specific collection of images, and hence achieve better accuracy. The core of our detector adaptation algorithm is a probabilistic elastic part (PEP) model, which is offline trained with a set of face examples. It produces a statistically aligned part based face representation, namely the PEP representation. To adapt a general face detector to a collection of images, we compute the PEP representations of the candidate detections from the general face detector, and then train a discriminative classifier with the top positives and negatives. Then we re-rank all the candidate detections with this classifier. This way, a face detector tailored to the statistics of the specific image collection is adapted from the original detector. We present extensive results on three datasets with two state-of-the-art face detectors. The significant improvement of detection accuracy over these state of-the-art face detectors strongly demonstrates the efficacy of the proposed face detector adaptation algorithm.

Context aware topic model for scene recognition

Abstract: We present a discriminative latent topic model for scene recognition. The capacity of our model is originated from the modeling of two types of visual contexts, i.e., the category specific global spatial layout of different scene elements, and the reinforcement of the visual coherence in uniform local regions. In contrast, most previous methods for scene recognition either only modeled one of these two visual contexts, or just totally ignored both of them. We cast these two coupled visual contexts in a discriminative Latent Dirichlet Allocation framework, namely context aware topic model. Then scene recognition is achieved by Bayesian inference given a target image. Our experiments on several scene recognition benchmarks clearly demonstrated the advantages of the proposed model.