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.

Decentralized Operation of Interdependent Power Distribution Network and District Heating Network: A Market-Driven Approach

Abstract: Combined harnessing of electrical and thermal energies could leverage their complementary nature, inspiring the integration of power grids and centralized heating systems in future smart cities. This paper considers interconnected power distribution network (PDN) and district heating network (DHN) infrastructures through combined heat and power units and heat pumps. In the envisioned market framework, the DHN operator solves an optimal thermal flow problem given the nodal electricity prices and determines the best heat production strategy. Variate coefficients of performance of heat pumps with respect to different load levels are considered and modeled in a disciplined convex optimization format. A two-step hydraulic-thermal decomposition method is suggested to approximately solve the optimal thermal flow problem via a second-order cone program. Simultaneously, the PDN operator clears the distribution power market via an optimal power flow problem given the demands from the DHN. Electricity prices are revealed by dual variables at the optimal solution. The whole problem gives rise to a Nash-type game between the two systems. A best-response decentralized algorithm is proposed to identify the optimal operation schedule of the coupled infrastructure, which interprets a market equilibrium as neither system has an incentive to alter their strategies. Numeric results demonstrate the potential benefits of the proposed framework in terms of reducing wind curtailment and system operation cost.

Estimation and equalization of fading channels with random coefficients

Abstract: The time-varying tap coefficients of frequency selective fading channels are typically modeled as random processes with low-pass power spectra. However, traditional adaptive techniques usually make no assumption on the channel's time variations and hence do not exploit this information. Kalman filtering methods are derived to track the channel by employing a multichannel autoregressive description of the time-varying taps in a decision-feedback equalization framework. Fitting a model to the variations of the channel's taps is a challenging task because the tap coefficients are not observed directly. A novel linear method is proposed in order to estimate the model parameters from input/output data. The consistency of the proposed method is shown, and some illustrative simulations are presented.

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Abstract: Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

Cosmological helium production simplified

Abstract: The authors present a simplified model of helium synthesis in the early universe. The purpose of the model is to explain clearly the physical ideas relevant to the cosmological helium synthesis in a manner that does not overlay these ideas with complex computer calculations. The model closely follows the standard calculation, except that it neglects the small effect of Fermi-Dirac statistics for the leptons. The temperature difference between photons and neutrinos during the period in which neutrons and protons interconvert is also neglected. These approximations permit the expression of neutron-proton conversion rates in a closed form, which agrees to 10% accuracy or better with the exact rates. Using these analytic expressions for the rates, the authors reduce the calculation of the neutron-proton ratio as a function of temperature to a simple numerical integral. They also estimate the effect of neutron decay on the helium abundance. Their result for this quantity agrees well with precise computer calculations. Their semianalytic formulas are used to determine how the predicted helium abundance varies with such parameters as the neutron lifetime, the baryon-to-photon ratio, the number of neutrino species, and a possible electron-neutrino chemical potential.