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.

Stock Market Liquidity and Firm Dividend Policy

Abstract: We provide evidence of a link between firm dividend policy and stock market liquidity. In the cross-section, owners of less (more) liquid common stock are more (less) likely to receive cash dividends. Over time, the notable increase in US stock market liquidity explains most of the declining propensity of firms to pay dividends documented by Fama and French (2001). We further show that past liquidity is an important determinant of dividend initiations and omissions for individual firms. Extending our analysis, we find evidence that sensitivity of firm value to innovations in aggregate liquidity declines after dividend initiations.

Microwave-enhanced enzyme reaction for protein mapping by mass spectrometry: A new approach to protein digestion in minutes

Abstract: Accelerated proteolytic cleavage of proteins under controlled microwave irradiation has been achieved. Selective peptide fragmentation by endoproteases trypsin or lysine C led to smaller peptides that were analyzed by matrix-assisted laser desorption ionization (MALDI) or liquid chromatography-electrospray ionization (LC-ESI) techniques. The efficacy of this technique for protein mapping was demonstrated by the mass spectral analyses of the peptide fragmentation of several biologically active proteins, including cytochrome c, ubiquitin, lysozyme, myoglobin, and interferon α-2b. Most important, using this novel approach digestion of proteins occurs in minutes, in contrast to the hours required by conventional methods.

Temperature-Dependent Electrical Properties of Graphene Inkjet-Printed on Flexible Materials

Abstract: Graphene electrode was fabricated by inkjet printing, as a new means of directly writing and micropatterning the electrode onto flexible polymeric materials. Graphene oxide sheets were dispersed in water and subsequently reduced using an infrared heat lamp at a temperature of ~200 °C in 10 min. Spacing between adjacent ink droplets and the number of printing layers were used to tailor the electrode's electrical sheet resistance as low as 0.3 MΩ/□ and optical transparency as high as 86%. The graphene electrode was found to be stable under mechanical flexing and behave as a negative temperature coefficient (NTC) material, exhibiting rapid electrical resistance decrease with temperature increase. Temperature sensitivity of the graphene electrode was similar to that of conventional NTC materials, but with faster response time by an order of magnitude. This finding suggests the potential use of the inkjet-printed graphene electrode as a writable, very thin, mechanically flexible, and transparent temperature sensor.

Priority-based congestion control in wireless sensor networks

Abstract: In wireless sensor networks (WSNs), congestion occurs, for example, when nodes are densely distributed, and/or the application produces high flow rate near the sink due to the convergent nature of upstream traffic. Congestion may cause packet loss, which in turn lowers throughput and wastes energy. Therefore congestion in WSNs needs to be controlled for high energy-efficiency, to prolong system lifetime, improve fairness, and improve quality of service (QoS) in terms of throughput (or link utilization) and packet loss ratio along with the packet delay. This paper proposes a node priority-based congestion control protocol (PCCP) for wireless sensor networks. In PCCP, node priority index is introduced to reflect the importance of each node. PCCP uses packet inter-arrival time along with packet service time to measure a parameter defined as congestion degree and furthermore imposes hop-by-hop control based on the measured congestion degree as well as the node priority index. PCCP controls congestion faster and more energy-efficiency than other known techniques.

Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT

Abstract: User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This paper supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV and thermostat, etc. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep learning based user authentication scheme to accurately identify each individual user. Extensive experiments in two typical indoor environments, a university office and an apartment, are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% and 91% authentication accuracy with 11 subjects through walking and stationary activities, respectively.